:: JGO :: Journal of Gynecologic Oncology

<!DOCTYPE html> <html lang="en" xmlns:mml="http://www.w3.org/1998/Math/MathML"> <head> <meta charset="utf-8"> <meta name="format-detection" content="telephone=no"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="citation_journal_title" content="Journal of Gynecologic Oncology"/> <meta name="citation_title" content="The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis"/> <meta name="citation_author" content="Zhao, Mengli"/> <meta name="citation_author_institution" content="Department of Radiation Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai,"> <meta name="citation_author" content="Li, Zhen"/> <meta name="citation_author_institution" content="ENT institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai,"> <meta name="citation_author" content="Gu, Xiaowei"/> <meta name="citation_author_institution" content="Department of Radiation Oncology, Jiangyin Hospital Affiliated to Nantong University, Jiangyin,"> <meta name="citation_author" content="Yang, Xiaojing"/> <meta name="citation_author_institution" content="Department of Radiation Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai,"> <meta name="citation_author" content="Gao, Zhongrong"/> <meta name="citation_author_institution" content="Department of Radiation Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai,"> <meta name="citation_author" content="Wang, Shanshan"/> <meta name="citation_author_institution" content="Department of Radiation Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai,"> <meta name="citation_author" content="Fu, Jie"/> <meta name="citation_author_institution" content="Department of Radiation Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai,"> <meta name="citation_author_email" content="fujie74@sjtu.edu.cn"> <meta name="citation_date" content="2024/05/01"/> <meta name="citation_volume" content="36"/> <meta name="citation_doi" content="10.3802/jgo.2025.36.e26"/> <meta name="citation_issn" content="2005-0399"/> <meta name="citation_pdf_url" content="http://www.ejgo.org/Synapse/Data/PDFData/1114JGO/jgo-36-e26.pdf"/> <meta name="citation_language" content="en"/> <meta name="citation_keywords" content="Cervical cancer; Radiomics; Deep learning; Artificial intelligence; Magnetic resonance imaging; Lymph-Vascular Space Infiltration"/> <meta name="citation_reference" content="citation_title=Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries; citation_author=H Sung; citation_author=J Ferlay; citation_author=RL Siegel; citation_author=M Laversanne; citation_author=I Soerjomataram; citation_author=A Jemal; citation_journal_title=CA Cancer J Clin; citation_volume=71; citation_pages=209-249; citation_date=2021;"/> <meta name="citation_reference" content="citation_title=Randomised study of radical surgery versus radiotherapy for stage Ib-IIa cervical cancer; citation_author=F Landoni; citation_author=A Maneo; citation_author=A Colombo; citation_author=F Placa; citation_author=R Milani; citation_author=P Perego; citation_journal_title=Lancet; citation_volume=350; citation_pages=535-540; citation_date=1997;"/> <meta name="citation_reference" content="citation_title=Randomized study between radical surgery and radiotherapy for the treatment of stage IB-IIA cervical cancer: 20-year update; citation_author=F Landoni; citation_author=A Colombo; citation_author=R Milani; citation_author=F Placa; citation_author=V Zanagnolo; citation_author=C Mangioni; citation_journal_title=J Gynecol Oncol; citation_volume=28; citation_pages=e34; citation_date=2017;"/> <meta name="citation_reference" content="citation_title=NCCN guidelines insights: cervical cancer, version 1.2020; citation_author=NR Abu-Rustum; citation_author=CM Yashar; citation_author=S Bean; citation_author=K Bradley; citation_author=SM Campos; citation_author=HS Chon; citation_journal_title=J Natl Compr Canc Netw; citation_volume=18; citation_pages=660-666; citation_date=2020;"/> <meta name="citation_reference" content="citation_title=Lymphatic metastasis in the absence of functional intratumor lymphatics; citation_author=TP Padera; citation_author=A Kadambi; citation_author=E di Tomaso; citation_author=CM Carreira; citation_author=EB Brown; citation_author=Y Boucher; citation_journal_title=Science; citation_volume=296; citation_pages=1883-1886; citation_date=2002;"/> <meta name="citation_reference" content="citation_title=Satellite lymphovascular space invasion: an independent risk factor in early stage cervical cancer; citation_author=FJ Pol; citation_author=PL Zusterzeel; citation_author=MA van Ham; citation_author=DA Kuijpers; citation_author=J Bulten; citation_author=LF Massuger; citation_journal_title=Gynecol Oncol; citation_volume=138; citation_pages=579-584; citation_date=2015;"/> <meta name="citation_reference" content="citation_title=Cervical biopsy and excision procedure specimens lack sufficient predictive value for lymph-vascular space invasion seen at hysterectomy for cervical cancer; citation_author=MA Bidus; citation_author=AS Caffrey; citation_author=WB You; citation_author=CA Amezcua; citation_author=MR Chernofsky; citation_author=R Barner; citation_journal_title=Am J Obstet Gynecol; citation_volume=199; citation_pages=151.e1-151.e4; citation_date=2008;"/> <meta name="citation_reference" content="citation_title=Editorial for: “Novel T2 Mapping for Evaluating Cervical Cancer Features by Providing Quantitative T2 Maps and Synthetic Morphologic Images: A Preliminary Study”; citation_author=F Alessandrino; citation_journal_title=J Magn Reson Imaging; citation_volume=52; citation_pages=1870-1871; citation_date=2020;"/> <meta name="citation_reference" content="citation_title=Radiomics: images are more than pictures, they are data; citation_author=RJ Gillies; citation_author=PE Kinahan; citation_author=H Hricak; citation_journal_title=Radiology; citation_volume=278; citation_pages=563-577; citation_date=2016;"/> <meta name="citation_reference" content="citation_title=Radiomics: extracting more information from medical images using advanced feature analysis; citation_author=P Lambin; citation_author=E Rios-Velazquez; citation_author=R Leijenaar; citation_author=S Carvalho; citation_author=RG van Stiphout; citation_author=P Granton; citation_journal_title=Eur J Cancer; citation_volume=48; citation_pages=441-446; citation_date=2012;"/> <meta name="citation_reference" content="citation_title=Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach; citation_author=HJ Aerts; citation_author=ER Velazquez; citation_author=RT Leijenaar; citation_author=C Parmar; citation_author=P Grossmann; citation_author=S Carvalho; citation_journal_title=Nat Commun; citation_volume=5; citation_pages=4006; citation_date=2014;"/> <meta name="citation_reference" content="citation_title=The potential of radiomic-based phenotyping in precision medicine: a review; citation_author=HJ Aerts; citation_journal_title=JAMA Oncol; citation_volume=2; citation_pages=1636-1642; citation_date=2016;"/> <meta name="citation_reference" content="citation_title=Radiomics: the bridge between medical imaging and personalized medicine; citation_author=P Lambin; citation_author=RT Leijenaar; citation_author=TM Deist; citation_author=J Peerlings; citation_author=EE de Jong; citation_author=J van Timmeren; citation_journal_title=Nat Rev Clin Oncol; citation_volume=14; citation_pages=749-762; citation_date=2017;"/> <meta name="citation_reference" content="citation_title=Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme; citation_author=EI Zacharaki; citation_author=S Wang; citation_author=S Chawla; citation_author=D Soo Yoo; citation_author=R Wolf; citation_author=ER Melhem; citation_journal_title=Magn Reson Med; citation_volume=62; citation_pages=1609-1618; citation_date=2009;"/> <meta name="citation_reference" content="citation_title=Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study; citation_author=H Wang; citation_author=X Xu; citation_author=X Zhang; citation_author=Y Liu; citation_author=L Ouyang; citation_author=P Du; citation_journal_title=Eur Radiol; citation_volume=30; citation_pages=4816-4827; citation_date=2020;"/> <meta name="citation_reference" content="citation_title=Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer; citation_author=X Deng; citation_author=M Liu; citation_author=J Sun; citation_author=M Li; citation_author=D Liu; citation_author=L Li; citation_journal_title=Eur J Radiol; citation_volume=134; citation_pages=109429; citation_date=2021;"/> <meta name="citation_reference" content="citation_title=Development of a deep learning model to identify lymph node metastasis on magnetic resonance imaging in patients with cervical cancer; citation_author=Q Wu; citation_author=S Wang; citation_author=S Zhang; citation_author=M Wang; citation_author=Y Ding; citation_author=J Fang; citation_journal_title=JAMA Netw Open; citation_volume=3; citation_pages=e2011625; citation_date=2020;"/> <meta name="citation_reference" content="citation_title=Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI; citation_author=C Liu; citation_author=J Ding; citation_author=K Spuhler; citation_author=Y Gao; citation_author=M Serrano Sosa; citation_author=M Moriarty; citation_journal_title=J Magn Reson Imaging; citation_volume=49; citation_pages=131-140; citation_date=2019;"/> <meta name="citation_reference" content="citation_title=Radiomics-based preoperative prediction of lymph node status following neoadjuvant therapy in locally advanced rectal cancer; citation_author=X Zhou; citation_author=Y Yi; citation_author=Z Liu; citation_author=Z Zhou; citation_author=B Lai; citation_author=K Sun; citation_journal_title=Front Oncol; citation_volume=10; citation_pages=604; citation_date=2020;"/> <meta name="citation_reference" content="citation_title=Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics; citation_author=Z Liu; citation_author=B Feng; citation_author=C Li; citation_author=Y Chen; citation_author=Q Chen; citation_author=X Li; citation_journal_title=J Magn Reson Imaging; citation_volume=50; citation_pages=847-857; citation_date=2019;"/> <meta name="citation_reference" content="citation_title=QUADAS-C: a tool for assessing risk of bias in comparative diagnostic accuracy studies; citation_author=B Yang; citation_author=S Mallett; citation_author=Y Takwoingi; citation_author=CF Davenport; citation_author=CJ Hyde; citation_author=PF Whiting; citation_journal_title=Ann Intern Med; citation_volume=174; citation_pages=1592-1599; citation_date=2021;"/> <meta name="citation_reference" content="citation_title=Statistical aspects of the analysis of data from retrospective studies of disease; citation_author=N Mantel; citation_author=W Haenszel; citation_journal_title=J Natl Cancer Inst; citation_volume=22; citation_pages=719-748; citation_date=1959;"/> <meta name="citation_reference" content="citation_title=The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed; citation_author=JJ Deeks; citation_author=P Macaskill; citation_author=L Irwig; citation_journal_title=J Clin Epidemiol; citation_volume=58; citation_pages=882-893; citation_date=2005;"/> <meta name="citation_reference" content="citation_title=Preoperative prediction of lymphovascular space invasion in cervical cancer with radiomics -based nomogram; citation_author=W Du; citation_author=Y Wang; citation_author=D Li; citation_author=X Xia; citation_author=Q Tan; citation_author=X Xiong; citation_journal_title=Front Oncol; citation_volume=11; citation_pages=637794; citation_date=2021;"/> <meta name="citation_reference" content="citation_title=MRI-based intratumoral and peritumoral radiomics on prediction of lymph-vascular space invasion in cervical cancer: a multi-center study; citation_author=J Shi; citation_author=L Cui; citation_author=H Wang; citation_author=Y Dong; citation_author=T Yu; citation_author=H Yang; citation_journal_title=Biomed Signal Process Control; citation_volume=72; citation_date=2022;"/> <meta name="citation_reference" content="citation_title=MR-based radiomics nomogram of cervical cancer in prediction of the lymph-vascular space invasion preoperatively; citation_author=Z Li; citation_author=H Li; citation_author=S Wang; citation_author=D Dong; citation_author=F Yin; citation_author=A Chen; citation_journal_title=J Magn Reson Imaging; citation_volume=49; citation_pages=1420-1426; citation_date=2019;"/> <meta name="citation_reference" content="citation_title=Lymph-vascular space invasion prediction in cervical cancer: Exploring radiomics and deep learning multilevel features of tumor and peritumor tissue on multiparametric MRI; citation_author=WQ Hua; citation_author=TH Xiao; citation_author=XR Jiang; citation_author=Z Liu; citation_author=M Wang; citation_author=H Zheng; citation_journal_title=Biomed Signal Process Control; citation_volume=58; citation_pages=101869; citation_date=2020;"/> <meta name="citation_reference" content="citation_title=Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer; citation_author=L Cui; citation_author=T Yu; citation_author=Y Kan; citation_author=Y Dong; citation_author=Y Luo; citation_author=X Jiang; citation_journal_title=Diagn Interv Radiol; citation_volume=28; citation_pages=312-321; citation_date=2022;"/> <meta name="citation_reference" content="citation_title=Radiomics analysis of multiparametric MRI evaluates the pathological features of cervical squamous cell carcinoma; citation_author=Q Wu; citation_author=D Shi; citation_author=S Dou; citation_author=L Shi; citation_author=M Liu; citation_author=L Dong; citation_journal_title=J Magn Reson Imaging; citation_volume=49; citation_pages=1141-1148; citation_date=2019;"/> <meta name="citation_reference" content="citation_title=Multi-parametric magnetic resonance imaging-based radiomics analysis of cervical cancer for preoperative prediction of lymphovascular space invasion; citation_author=G Huang; citation_author=Y Cui; citation_author=P Wang; citation_author=J Ren; citation_author=L Wang; citation_author=Y Ma; citation_journal_title=Front Oncol; citation_volume=11; citation_pages=663370; citation_date=2022;"/> <meta name="citation_reference" content="citation_title=Multiparametric MRI radiomics nomogram for predicting lymph-vascular space invasion in early-stage cervical cancer; citation_author=M Xiao; citation_author=Y Li; citation_author=F Ma; citation_author=G Zhang; citation_author=J Qiang; citation_journal_title=Br J Radiol; citation_volume=95; citation_pages=20211076; citation_date=2022;"/> <meta name="citation_reference" content="citation_title=A multicenter study on preoperative assessment of lymphovascular space invasion in early-stage cervical cancer based on multimodal MR radiomics; citation_author=Y Wu; citation_author=S Wang; citation_author=Y Chen; citation_author=Y Liao; citation_author=X Yin; citation_author=T Li; citation_journal_title=J Magn Reson Imaging; citation_volume=58; citation_pages=1638-1648; citation_date=2023;"/> <meta name="citation_reference" content="citation_title=Prognostic significance of lymphovascular space invasion for stage IA1 and IA2 cervical cancer; citation_author=B Margolis; citation_author=K Cagle-Colon; citation_author=L Chen; citation_author=AI Tergas; citation_author=L Boyd; citation_author=JD Wright; citation_journal_title=Int J Gynecol Cancer; citation_volume=30; citation_pages=735-743; citation_date=2020;"/> <meta name="citation_reference" content="citation_title=Prospective surgical-pathological study of disease-free interval in patients with stage IB squamous cell carcinoma of the cervix: a Gynecologic Oncology Group study; citation_author=G Delgado; citation_author=B Bundy; citation_author=R Zaino; citation_author=BU Sevin; citation_author=WT Creasman; citation_author=F Major; citation_journal_title=Gynecol Oncol; citation_volume=38; citation_pages=352-357; citation_date=1990;"/> <meta name="citation_reference" content="citation_title=Radiomics and artificial intelligence for biomarker and prediction model development in oncology; citation_author=R Forghani; citation_author=P Savadjiev; citation_author=A Chatterjee; citation_author=N Muthukrishnan; citation_author=C Reinhold; citation_author=B Forghani; citation_journal_title=Comput Struct Biotechnol J; citation_volume=17; citation_pages=995-1008; citation_date=2019;"/> <meta name="citation_reference" content="citation_title=Deep learning predicts lung cancer treatment response from serial medical imaging; citation_author=Y Xu; citation_author=A Hosny; citation_author=R Zeleznik; citation_author=C Parmar; citation_author=T Coroller; citation_author=I Franco; citation_journal_title=Clin Cancer Res; citation_volume=25; citation_pages=3266-3275; citation_date=2019;"/> <meta name="citation_reference" content="citation_title=Introduction to radiomics; citation_author=ME Mayerhoefer; citation_author=A Materka; citation_author=G Langs; citation_author=I Häggström; citation_author=P Szczypiński; citation_author=P Gibbs; citation_journal_title=J Nucl Med; citation_volume=61; citation_pages=488-495; citation_date=2020;"/> <meta name="citation_reference" content="citation_title=A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer; citation_author=L Li; citation_author=J Zhang; citation_author=X Zhe; citation_author=M Tang; citation_author=X Zhang; citation_author=X Lei; citation_journal_title=Eur J Radiol; citation_volume=151; citation_pages=110243; citation_date=2022;"/> <meta name="citation_reference" content="citation_title=Decision support systems for personalized and participative radiation oncology; citation_author=P Lambin; citation_author=J Zindler; citation_author=BG Vanneste; citation_author=LV De Voorde; citation_author=D Eekers; citation_author=I Compter; citation_journal_title=Adv Drug Deliv Rev; citation_volume=109; citation_pages=131-153; citation_date=2017;"/> <meta name="citation_reference" content="citation_title=Multiparametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in endometrial carcinoma; citation_author=Y Luo; citation_author=D Mei; citation_author=J Gong; citation_author=M Zuo; citation_author=X Guo; citation_journal_title=J Magn Reson Imaging; citation_volume=52; citation_pages=1257-1262; citation_date=2020;"/> <meta name="citation_reference" content="citation_title=Diagnostic accuracy of deep learning and radiomics in lung cancer staging: a systematic review and meta-analysis; citation_author=X Zheng; citation_author=B He; citation_author=Y Hu; citation_author=M Ren; citation_author=Z Chen; citation_author=Z Zhang; citation_journal_title=Front Public Health; citation_volume=10; citation_pages=938113; citation_date=2022;"/> <meta name="citation_reference" content="citation_title=Prostate MRI radiomics: a systematic review and radiomic quality score assessment; citation_author=A Stanzione; citation_author=M Gambardella; citation_author=R Cuocolo; citation_author=A Ponsiglione; citation_author=V Romeo; citation_author=M Imbriaco; citation_journal_title=Eur J Radiol; citation_volume=129; citation_pages=109095; citation_date=2020;"/> <meta property="og:title" content="The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis"> <meta property="og:url" content="https://doi.org/10.3802/jgo.2025.36.e26"> <meta property="og:image" content="http://www.ejgo.org/image/synapse-logo.gif"> <meta property="og:type" content="article"> <meta property="og:site_name" content="https://doi.org/10.3802/jgo.2025.36.e26"> <meta property="og:description" content="Zhao M, et al. J Gynecol Oncol. 2024 Jul;36:e26. https://doi.org/10.3802/jgo.2025.36.e26"> <meta name="dc.identifier" content="doi:10.3802/jgo.2025.36.e26"> <title>:: JGO :: Journal of Gynecologic Oncology</title>  <link rel="stylesheet" href="css/bootstrap5.custom.css"> <link href="https://cdn.jsdelivr.net/gh/kartik-v/bootstrap-popover-x@1.5.1/css/bootstrap-popover-x.min.css" media="all" rel="stylesheet" type="text/css" /> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap-icons@1.11.3/font/bootstrap-icons.css"> <link rel="stylesheet" href="css/nouislider.min.css"> <link rel="stylesheet" href="css/chartist.css"> <link rel="stylesheet" href="css/chartist-plugin-tooltip.css"> <link rel="stylesheet" href="fonts/IBM-plex/css/ibm-plex.css" /> <link rel="stylesheet" href="fonts/IBM-plex/css/ibm-plex-sans-kr.css" /> <link rel="stylesheet" href="css/default.css"> <link rel="shortcut icon" href="favicon-jgo.ico"> </style>   <script async src="https://www.googletagmanager.com/gtag/js?id=UA-100755984-38"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-100755984-38'); </script>  </head> <body class="d-flex flex-column "> <header class="masthead masthead sticky-top bg-white"> <div class="container-fluid"> <div class="header"> <div class="container"> <a href="/"> <svg xmlns="http://www.w3.org/2000/svg" id="Layer_2" data-name="Layer 2" viewBox="0 0 113.79 53.3"> <defs> <style> .cls-1 { fill: #59513e; } .cls-2left { fill: #7ec5e8; } </style> </defs> <g id="_레이어_1" data-name="레이어 1"> <path class="cls-1" d="m96.39,9.69c.1,3.23-.13,5.95-.69,8.17-.56,2.22-1.3,4.01-2.24,5.39-.94,1.37-2.05,2.35-3.33,2.93-1.28.58-2.59.87-3.94.87-2.84,0-5.1-.82-6.76-2.46-1.66-1.64-2.45-3.83-2.35-6.58.1-3.08,1.06-5.37,2.89-6.87,1.83-1.49,4.24-2.24,7.23-2.24,1.5,0,3.04.07,4.63.22,1.59.14,3.11.34,4.56.58m-10.2,26.18c6.56,0,11.61-2.27,15.15-6.8,3.54-4.53,5.32-10.87,5.32-19.02,0-1.11-.04-2.22-.11-3.33-.07-1.11-.16-2.19-.25-3.25-3.13-1.11-6.47-1.95-10.02-2.53-3.54-.58-6.9-.87-10.09-.87-2.6,0-5.07.37-7.41,1.12-2.34.75-4.39,1.87-6.15,3.36-1.76,1.49-3.17,3.36-4.23,5.6-1.06,2.24-1.59,4.86-1.59,7.85,0,2.36.47,4.63,1.41,6.8.94,2.17,2.25,4.07,3.94,5.71,1.69,1.64,3.72,2.94,6.11,3.91,2.39.96,5.03,1.45,7.92,1.45Z"/> <path class="cls-1" d="m45.99,17.43c.92-.05,1.82-.14,2.71-.29.89-.14,1.67-.39,2.35-.72.68-.34,1.22-.77,1.63-1.3.41-.53.61-1.21.61-2.03,0-.87-.24-1.58-.72-2.13-.48-.55-1.12-1-1.92-1.34-.8-.34-1.68-.57-2.64-.69-.96-.12-1.95-.18-2.97-.18s-2.01.06-3,.18c-.99.12-1.88.35-2.68.69-.8.34-1.43.78-1.92,1.34-.48.55-.72,1.27-.72,2.13s.23,1.58.69,2.13c.46.55,1.06,1,1.81,1.34.75.34,1.59.58,2.53.72.94.14,1.89.22,2.86.22.19,0,.47-.02.69-.02l.69-.05Zm17.57-4.34c0,1.93-.46,3.69-1.37,5.28-.92,1.59-2.19,2.95-3.83,4.09-1.64,1.13-3.56,2.03-5.75,2.68-2.19.65-4.54,1-7.05,1.05-.87,0-1.69.13-2.46.25-.77.12-1.46.28-2.06.47-.6.19-1.09.43-1.45.72-.36.29-.54.6-.54.94,0,.67.51,1.22,1.52,1.63,1.01.41,2.29.82,3.83,1.23,1.54.41,3.21.86,4.99,1.34,1.78.48,3.45,1.13,4.99,1.95,1.54.82,2.82,1.88,3.83,3.18,1.01,1.3,1.52,2.97,1.52,4.99,0,2.31-.63,4.16-1.88,5.53-1.25,1.37-2.76,2.42-4.52,3.15-1.76.72-3.59,1.19-5.5,1.41-1.9.22-3.51.33-4.81.33-3.18,0-6-.31-8.46-.94-2.46-.63-4.46-1.21-6-1.74v-8.32c1.59.39,3.59.84,6,1.37,2.41.53,4.89.79,7.45.79,1.59,0,2.98-.1,4.16-.29,1.18-.19,1.77-.63,1.77-1.3,0-.58-.52-1.06-1.56-1.45-1.04-.38-2.34-.77-3.9-1.16-1.57-.39-3.25-.83-5.06-1.34-1.81-.51-3.48-1.19-5.03-2.06-1.54-.87-2.81-1.97-3.8-3.29-.99-1.33-1.43-3-1.34-5.03.1-1.45.46-2.74,1.08-3.87.63-1.13,1.45-2.11,2.46-2.93-1.4-1.11-2.47-2.4-3.22-3.87-.75-1.47-1.12-3.07-1.12-4.81,0-1.93.48-3.7,1.45-5.31.96-1.62,2.28-3,3.94-4.16,1.66-1.16,3.63-2.05,5.89-2.68,2.26-.63,4.7-.94,7.3-.94s5.03.31,7.27.94c2.24.63,4.19,1.52,5.86,2.68,1.66,1.16,2.98,2.54,3.94,4.16.96,1.62,1.45,3.39,1.45,5.31Z"/> <path class="cls-1" d="m11.21,25.89c0-2.94-.27-5.89-.8-8.86-.53-2.96-1.13-5.65-1.81-8.06-.77-2.8-1.64-5.5-2.6-8.1h10.92c.82,2.51,1.57,5.13,2.24,7.88.53,2.36,1.04,5.03,1.52,7.99.48,2.97.72,6.01.72,9.15,0,3.57-.45,6.91-1.34,10.01-.89,3.11-2.21,5.85-3.94,8.21-1.74,2.36-3.87,4.22-6.4,5.57-2.53,1.35-5.44,2.02-8.71,2.02H0v-8.75c1.4-.1,2.77-.42,4.12-.98,1.35-.55,2.54-1.48,3.58-2.78,1.04-1.3,1.88-3.03,2.53-5.17.65-2.15.98-4.86.98-8.13"/> <path class="cls-2left" d="m73.62,38.15c6.92,3.12,13.04,3.87,18.45,2.67,5.77-1.29,10.92-4.87,15.52-10.26l6.2,5.31c-5.72,6.7-12.33,11.2-19.96,12.91-7.12,1.59-14.95.7-23.56-3.18l3.36-7.45Z"/> </g> </svg> <svg class="d-none d-lg-block" xmlns="http://www.w3.org/2000/svg" id="_레이어_2" viewBox="0 0 396.97 73.9"> <defs> <style>.cls-3,.cls-4,.cls-5{fill-rule:evenodd;}.cls-6,.cls-3{fill:#1e95d4;}.cls-2,.cls-7{fill:#857154;}.cls-8{fill:#59503e;}.cls-9{fill:none;stroke:#717071;stroke-miterlimit:22.93;stroke-width:1px;}.cls-10{fill:#7ec5e8;}.cls-4{fill:#003d8b;}.cls-5{fill:#6bb7df;}</style> </defs> <line class="cls-9" x1="129.43" x2="129.43" y2="73.9"/> <path class="cls-3" d="M174.48,4.9c-17.7,0-32.05,14.35-32.05,32.05,0,6.41,1.88,12.37,5.12,17.38,10.06-1.85,18.91-7.17,25.25-14.68-3.37-3.18-5.47-7.68-5.47-12.68,0-9.03,6.86-16.45,15.66-17.34-.06-1.24-.17-2.47-.32-3.68-2.61-.69-5.36-1.05-8.18-1.05"/> <path class="cls-4" d="M182.99,9.63c-8.79,.89-15.66,8.31-15.66,17.34,0,5,2.1,9.5,5.47,12.68,6.39-7.56,10.24-17.32,10.24-27.99,0-.68-.02-1.36-.05-2.03"/><path class="cls-2" d="M183.04,11.66c0,10.67-3.85,20.44-10.24,27.99,3.12,2.95,7.33,4.75,11.96,4.75,9.63,0,17.43-7.8,17.43-17.43s-7.8-17.43-17.43-17.43c-.6,0-1.19,.03-1.77,.09,.03,.67,.05,1.35,.05,2.03"/> <path class="cls-5" d="M168.98,55.94c0-4.4,3.56-7.96,7.96-7.96s7.96,3.56,7.96,7.96-3.56,7.96-7.96,7.96-7.96-3.56-7.96-7.96m15.78-11.53c-4.63,0-8.84-1.81-11.96-4.75-6.34,7.51-15.19,12.83-25.25,14.68,5.71,8.83,15.64,14.67,26.93,14.67,17.7,0,32.05-14.35,32.05-32.05,0-14.87-10.13-27.38-23.87-31,.16,1.21,.27,2.44,.32,3.68,.58-.06,1.17-.09,1.77-.09,9.63,0,17.43,7.8,17.43,17.43s-7.8,17.43-17.43,17.43Z"/> <path class="cls-1" d="M184.9,55.94c0-4.4-3.56-7.96-7.96-7.96s-7.96,3.56-7.96,7.96,3.56,7.96,7.96,7.96,7.96-3.56,7.96-7.96"/> <polygon class="cls-6" points="226.42 20.72 226.42 10.02 224.21 10.02 224.21 22.8 230.95 22.8 230.95 20.72 226.42 20.72"/> <path class="cls-6" d="M236.92,15c1.37,0,2.48,.87,2.82,2.17h-5.61c.35-1.3,1.43-2.17,2.8-2.17m5.06,3.09c0-2.83-2.17-4.96-5.06-4.96s-5.04,2.13-5.04,4.95,2.17,4.95,5.04,4.95c2.33,0,4.19-1.32,4.87-3.22h-2.26c-.41,.8-1.39,1.35-2.59,1.35-1.43,0-2.54-.91-2.83-2.28h7.82c.04-.22,.06-.57,.06-.78Z"/> <path class="cls-6" d="M246.81,21.24c-.93,0-1.63-.59-1.63-1.39s.7-1.37,1.63-1.37h2.2v1.24c0,.93-.89,1.52-2.2,1.52m4.28-.33v-7.56h-7.37v1.96h5.3v1.43h-2.56c-1.94,0-3.41,1.35-3.41,3.13s1.46,3.15,3.41,3.15c1.19,0,2.19-.5,2.8-1.3v1.07h2.98v-1.89h-1.15Z"/> <path class="cls-6" d="M258.2,21.02c-1.69,0-2.93-1.28-2.93-2.94s1.24-2.94,2.93-2.94,2.91,1.28,2.91,2.94-1.24,2.94-2.91,2.94m2.91-11.56v5.35c-.76-1.04-1.93-1.69-3.32-1.69-2.67,0-4.65,2.13-4.65,4.95s1.98,4.95,4.65,4.95c1.39,0,2.56-.65,3.32-1.69v1.46h2.07V9.46h-2.07Z"/> <polygon class="cls-6" points="275.61 15.24 275.61 13.35 273.15 13.35 273.15 10.58 271.07 10.58 271.07 13.35 269.31 13.35 269.31 15.24 271.07 15.24 271.07 22.8 275.61 22.8 275.61 20.89 273.15 20.89 273.15 15.24 275.61 15.24"/> <path class="cls-6" d="M281.5,21.02c-1.67,0-2.93-1.28-2.93-2.94s1.26-2.94,2.93-2.94,2.91,1.28,2.91,2.94-1.24,2.94-2.91,2.94m0,2c2.87,0,5.06-2.13,5.06-4.95s-2.19-4.95-5.06-4.95-5.06,2.13-5.06,4.95,2.17,4.95,5.06,4.95Z"/> <path class="cls-6" d="M292.94,21.02c-1.69,0-2.93-1.28-2.93-2.94s1.24-2.94,2.93-2.94,2.91,1.28,2.91,2.94-1.24,2.94-2.91,2.94m2.91-11.56v5.35c-.76-1.04-1.93-1.69-3.32-1.69-2.67,0-4.65,2.13-4.65,4.95s1.98,4.95,4.65,4.95c1.39,0,2.56-.65,3.32-1.69v1.46h2.07V9.46h-2.07Z"/> <path class="cls-6" d="M303.48,21.24c-.93,0-1.63-.59-1.63-1.39s.7-1.37,1.63-1.37h2.2v1.24c0,.93-.89,1.52-2.2,1.52m4.28-.33v-7.56h-7.37v1.96h5.3v1.43h-2.56c-1.94,0-3.41,1.35-3.41,3.13s1.46,3.15,3.41,3.15c1.19,0,2.19-.5,2.8-1.3v1.07h2.98v-1.89h-1.15Z"/> <polygon class="cls-6" points="317.17 13.35 314.43 20.24 311.67 13.35 309.3 13.35 313.28 22.72 312.54 24.41 310.65 24.41 310.65 26.32 313.98 26.32 319.48 13.35 317.17 13.35"/> <path class="cls-6" d="M230.27,42.08c3.28,0,5.87-2.09,6.56-5.06h-2.35c-.59,1.74-2.22,2.93-4.2,2.93-2.52,0-4.45-1.93-4.45-4.48s1.93-4.48,4.45-4.48c1.98,0,3.61,1.19,4.2,2.93h2.35c-.69-2.96-3.28-5.06-6.56-5.06-3.82,0-6.72,2.85-6.72,6.61s2.91,6.61,6.72,6.61"/> <path class="cls-6" d="M240.77,33.95v-1.54h-2.07v9.45h2.07v-4.59c0-1.74,1.07-2.87,2.91-2.87v-2.13c-1.2,0-2.2,.63-2.91,1.69"/> <path class="cls-6" d="M249.38,34.06c1.37,0,2.48,.87,2.81,2.17h-5.61c.35-1.3,1.43-2.17,2.8-2.17m5.06,3.09c0-2.83-2.17-4.96-5.06-4.96s-5.04,2.13-5.04,4.95,2.17,4.95,5.04,4.95c2.33,0,4.19-1.32,4.87-3.22h-2.26c-.41,.8-1.39,1.35-2.59,1.35-1.43,0-2.54-.91-2.83-2.28h7.82c.04-.22,.06-.57,.06-.78Z"/> <path class="cls-6" d="M259.27,40.3c-.93,0-1.63-.59-1.63-1.39s.7-1.37,1.63-1.37h2.2v1.24c0,.93-.89,1.52-2.2,1.52m4.28-.33v-7.56h-7.37v1.96h5.3v1.43h-2.56c-1.94,0-3.41,1.35-3.41,3.13s1.46,3.15,3.41,3.15c1.19,0,2.19-.5,2.8-1.3v1.07h2.98v-1.89h-1.15Z"/> <polygon class="cls-6" points="271.68 34.3 271.68 32.41 269.22 32.41 269.22 29.64 267.14 29.64 267.14 32.41 265.39 32.41 265.39 34.3 267.14 34.3 267.14 41.86 271.68 41.86 271.68 39.95 269.22 39.95 269.22 34.3 271.68 34.3"/> <path class="cls-6" d="M277.55,34.06c1.37,0,2.48,.87,2.82,2.17h-5.61c.35-1.3,1.43-2.17,2.8-2.17m5.06,3.09c0-2.83-2.17-4.96-5.06-4.96s-5.04,2.13-5.04,4.95,2.17,4.95,5.04,4.95c2.33,0,4.19-1.32,4.87-3.22h-2.26c-.41,.8-1.39,1.35-2.59,1.35-1.43,0-2.54-.91-2.83-2.28h7.82c.04-.22,.06-.57,.06-.78Z"/> <polygon class="cls-6" points="294.39 34.3 294.39 32.41 291.93 32.41 291.93 29.64 289.85 29.64 289.85 32.41 288.09 32.41 288.09 34.3 289.85 34.3 289.85 41.86 294.39 41.86 294.39 39.95 291.93 39.95 291.93 34.3 294.39 34.3"/> <path class="cls-6" d="M300.28,40.08c-1.67,0-2.93-1.28-2.93-2.94s1.26-2.94,2.93-2.94,2.91,1.28,2.91,2.94-1.24,2.94-2.91,2.94m0,2c2.87,0,5.06-2.13,5.06-4.95s-2.19-4.95-5.06-4.95-5.06,2.13-5.06,4.95,2.17,4.95,5.06,4.95Z"/> <path class="cls-6" d="M318.61,32.19c-1.46,0-2.59,.65-3.3,1.82-.54-1.13-1.65-1.82-3.11-1.82-1.24,0-2.24,.5-2.93,1.44v-1.22h-2.07v9.45h2.07v-5.48c0-1.32,.91-2.19,2.24-2.19s2.09,.85,2.09,2.15v5.52h2.07v-5.48c0-1.32,.91-2.19,2.24-2.19s2.09,.85,2.09,2.15v5.52h2.07v-6.13c0-2.13-1.39-3.54-3.48-3.54"/> <path class="cls-6" d="M328.89,40.08c-1.67,0-2.93-1.28-2.93-2.94s1.26-2.94,2.93-2.94,2.91,1.28,2.91,2.94-1.24,2.94-2.91,2.94m0,2c2.87,0,5.06-2.13,5.06-4.95s-2.19-4.95-5.06-4.95-5.06,2.13-5.06,4.95,2.17,4.95,5.06,4.95Z"/> <path class="cls-6" d="M337.89,33.95v-1.54h-2.07v9.45h2.07v-4.59c0-1.74,1.07-2.87,2.91-2.87v-2.13c-1.2,0-2.2,.63-2.91,1.69"/> <path class="cls-6" d="M344.54,33.95v-1.54h-2.07v9.45h2.07v-4.59c0-1.74,1.07-2.87,2.91-2.87v-2.13c-1.2,0-2.2,.63-2.91,1.69"/> <path class="cls-6" d="M353.17,40.08c-1.67,0-2.93-1.28-2.93-2.94s1.26-2.94,2.93-2.94,2.91,1.28,2.91,2.94-1.24,2.94-2.91,2.94m0,2c2.87,0,5.06-2.13,5.06-4.95s-2.19-4.95-5.06-4.95-5.06,2.13-5.06,4.95,2.17,4.95,5.06,4.95Z"/> <polygon class="cls-6" points="372.31 32.41 370.05 39.29 367.69 32.41 365.56 32.41 363.19 39.29 360.93 32.41 358.58 32.41 362.06 41.86 364.21 41.86 366.58 34.84 368.95 41.86 371.1 41.86 374.6 32.41 372.31 32.41"/><g> <path class="cls-7" d="M226.78,54.46c0,.53-.06,.98-.17,1.35-.11,.37-.28,.68-.48,.91-.21,.23-.46,.4-.76,.51-.3,.11-.64,.16-1.01,.16-.18,0-.35-.01-.51-.04-.16-.02-.31-.06-.44-.1-.14-.04-.26-.1-.37-.16s-.22-.13-.31-.2c-.08-.06-.15-.12-.19-.21-.05-.08-.07-.18-.07-.29,0-.15,.05-.28,.15-.39,.1-.11,.23-.16,.41-.16,.09,0,.17,.02,.23,.06,.07,.04,.13,.08,.19,.13,.09,.08,.2,.15,.33,.21,.13,.07,.31,.1,.53,.1s.4-.03,.56-.09,.29-.17,.39-.31c.11-.15,.19-.34,.24-.58,.05-.24,.08-.54,.08-.89v-3.84c0-.18,.06-.32,.17-.43,.11-.11,.26-.16,.46-.16,.18,0,.32,.05,.43,.16,.11,.11,.16,.25,.16,.43v3.84Z"/> <path class="cls-7" d="M233.24,55.63c-.13,.41-.31,.74-.55,1-.23,.26-.51,.45-.85,.58-.33,.12-.7,.19-1.1,.19s-.76-.06-1.09-.19c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19s.77,.06,1.1,.19c.33,.12,.62,.32,.85,.58,.23,.26,.41,.59,.55,1,.13,.41,.2,.89,.2,1.46v.92c0,.56-.07,1.05-.2,1.46Zm-3.97-1.46c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12s.46-.04,.64-.12,.34-.21,.46-.38c.12-.18,.22-.4,.29-.68,.07-.27,.1-.6,.1-.99v-.92c0-.39-.03-.72-.1-.99-.07-.27-.16-.5-.29-.68-.12-.18-.28-.31-.46-.38s-.4-.12-.64-.12-.45,.04-.63,.12c-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92Z"/> <path class="cls-7" d="M236.31,57.23c-.33-.11-.62-.29-.85-.54-.24-.24-.42-.55-.55-.93-.13-.38-.19-.83-.19-1.36v-3.76c0-.18,.05-.33,.16-.44,.1-.11,.25-.17,.45-.17,.18,0,.33,.06,.44,.17,.11,.11,.17,.26,.17,.44v3.76c0,.69,.12,1.18,.37,1.49,.25,.31,.62,.46,1.11,.46s.86-.15,1.11-.46c.25-.3,.37-.8,.37-1.49v-3.76c0-.18,.05-.33,.16-.44,.11-.11,.25-.17,.44-.17s.33,.06,.44,.17c.11,.11,.16,.26,.16,.44v3.75c0,.53-.06,.98-.19,1.36-.13,.38-.31,.69-.55,.94-.24,.24-.52,.42-.85,.54-.33,.11-.7,.17-1.09,.17s-.76-.06-1.09-.17Z"/> <path class="cls-7" d="M244.12,50.13c.83,0,1.47,.18,1.94,.54,.47,.36,.7,.89,.7,1.61,0,.5-.11,.91-.34,1.22-.23,.32-.55,.55-.97,.69l1.19,2.22c.03,.06,.07,.13,.09,.2s.04,.15,.04,.23c0,.15-.05,.28-.16,.39s-.24,.17-.42,.17c-.13,0-.25-.03-.34-.09-.1-.06-.18-.15-.24-.29l-1.33-2.63h-1.4v2.41c0,.18-.06,.32-.17,.43s-.26,.16-.45,.16-.33-.05-.44-.16c-.1-.11-.16-.25-.16-.43v-6.08c0-.18,.05-.32,.16-.43,.1-.11,.25-.16,.44-.16h1.85Zm-.08,3.22c.26,0,.48-.02,.67-.06,.19-.04,.35-.1,.47-.18,.13-.08,.22-.19,.29-.33s.09-.31,.09-.51-.03-.37-.09-.51-.16-.25-.29-.34-.29-.15-.47-.19c-.19-.04-.41-.06-.67-.06h-1.15v2.17h1.15Z"/><path class="cls-7" d="M252.19,50.19c.1-.1,.25-.16,.43-.16s.32,.05,.43,.16c.1,.1,.16,.25,.16,.43v6.16c0,.19-.06,.34-.17,.45-.11,.11-.26,.17-.44,.17s-.31-.04-.41-.11-.18-.17-.24-.28l-2.74-4.67v4.48c0,.18-.05,.32-.16,.43-.11,.1-.25,.16-.43,.16s-.31-.05-.42-.16c-.1-.1-.16-.25-.16-.43v-6.14c0-.19,.06-.35,.17-.47,.11-.12,.28-.18,.48-.18,.12,0,.23,.03,.33,.1,.1,.07,.19,.15,.25,.25l2.76,4.71v-4.48c0-.18,.05-.32,.16-.43Z"/> <path class="cls-7" d="M257.02,51.78l-1.7,5.19c-.04,.13-.11,.24-.2,.31s-.22,.11-.38,.11c-.17,0-.32-.05-.43-.16-.11-.1-.17-.24-.17-.41,0-.05,0-.1,0-.15,0-.05,.02-.11,.04-.16l2.06-5.89c.08-.22,.18-.37,.31-.46,.13-.09,.28-.14,.45-.14s.32,.05,.45,.13c.13,.09,.23,.25,.31,.47l2.05,5.87c.01,.06,.03,.11,.04,.17s.02,.11,.02,.16c0,.17-.05,.31-.16,.42-.1,.1-.24,.16-.42,.16-.16,0-.29-.04-.38-.11-.1-.08-.17-.18-.21-.31l-1.69-5.19Zm1.45,4.04h-2.9l.34-1.01h2.22l.34,1.01Z"/><path class="cls-7" d="M262.05,56.25h2.73c.16,0,.29,.05,.38,.15,.09,.1,.14,.23,.14,.38s-.05,.29-.14,.38-.22,.14-.38,.14h-3.34c-.19,0-.33-.05-.44-.16-.1-.11-.16-.25-.16-.43v-6.08c0-.18,.05-.32,.16-.43,.11-.11,.26-.16,.45-.16,.18,0,.32,.05,.43,.16,.11,.11,.16,.25,.16,.43v5.63Z"/><path class="cls-7" d="M273.62,55.63c-.13,.41-.31,.74-.55,1-.23,.26-.51,.45-.85,.58-.33,.12-.7,.19-1.1,.19s-.76-.06-1.09-.19c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19s.77,.06,1.1,.19c.33,.12,.62,.32,.85,.58,.23,.26,.41,.59,.55,1,.13,.41,.2,.89,.2,1.46v.92c0,.56-.07,1.05-.2,1.46Zm-3.97-1.46c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12s.46-.04,.64-.12,.34-.21,.46-.38c.12-.18,.22-.4,.29-.68,.07-.27,.1-.6,.1-.99v-.92c0-.39-.03-.72-.1-.99-.07-.27-.16-.5-.29-.68-.12-.18-.28-.31-.46-.38s-.4-.12-.64-.12-.45,.04-.63,.12c-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92Z"/><path class="cls-7" d="M279.05,50.13c.16,0,.29,.05,.38,.14s.15,.22,.15,.38-.05,.29-.15,.38c-.1,.1-.23,.15-.38,.15h-2.74v2.06h2.42c.16,0,.29,.05,.38,.14s.14,.22,.14,.38-.05,.29-.14,.38-.22,.14-.38,.14h-2.42v2.53c0,.18-.06,.32-.17,.43s-.26,.16-.45,.16-.33-.05-.44-.16c-.1-.11-.16-.25-.16-.43v-6.08c0-.18,.05-.32,.16-.43,.1-.11,.25-.16,.44-.16h3.35Z"/><path class="cls-7" d="M284.47,57.21c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.86c0-.58,.06-1.07,.19-1.49s.31-.76,.55-1.02c.24-.26,.52-.46,.85-.58,.33-.12,.7-.19,1.09-.19,.42,0,.78,.06,1.1,.19,.32,.12,.58,.28,.79,.47,.21,.19,.36,.39,.47,.61s.16,.42,.16,.62c0,.18-.05,.32-.16,.42-.1,.1-.23,.15-.38,.15s-.28-.04-.37-.11-.16-.18-.21-.32c-.08-.3-.22-.54-.45-.71-.22-.17-.53-.25-.92-.25-.24,0-.46,.04-.64,.12-.19,.08-.34,.21-.47,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12,.22,0,.42-.03,.59-.1,.17-.07,.32-.17,.44-.31s.22-.31,.29-.53c.07-.21,.12-.46,.13-.74h-1.01c-.17,0-.3-.05-.4-.14-.1-.09-.15-.22-.15-.38s.05-.29,.15-.38c.1-.1,.23-.15,.4-.15h1.62c.18,0,.32,.05,.42,.16,.1,.1,.15,.24,.15,.42,0,.56-.06,1.04-.19,1.45s-.3,.73-.53,.99c-.23,.26-.51,.45-.83,.58-.33,.12-.69,.19-1.09,.19s-.76-.06-1.09-.19Z"/><path class="cls-7" d="M288.97,50.18c.12-.1,.27-.16,.45-.16,.12,0,.23,.03,.32,.08,.09,.05,.16,.13,.21,.22l1.45,2.67,1.42-2.64c.05-.1,.12-.18,.21-.24,.09-.07,.2-.1,.34-.1,.17,0,.32,.05,.44,.16,.12,.11,.18,.25,.18,.42,0,.06,0,.12-.03,.18-.02,.06-.04,.11-.07,.16l-1.89,3.37v2.5c0,.18-.06,.32-.17,.43s-.26,.16-.46,.16c-.18,0-.32-.05-.43-.16s-.16-.25-.16-.43v-2.5l-1.87-3.34s-.05-.1-.08-.16c-.02-.06-.04-.13-.04-.2,0-.18,.06-.32,.18-.43Z"/><path class="cls-7" d="M299.2,50.19c.1-.1,.25-.16,.43-.16s.32,.05,.43,.16c.1,.1,.16,.25,.16,.43v6.16c0,.19-.06,.34-.17,.45-.11,.11-.26,.17-.44,.17s-.31-.04-.41-.11-.18-.17-.24-.28l-2.74-4.67v4.48c0,.18-.05,.32-.16,.43-.11,.1-.25,.16-.43,.16s-.31-.05-.42-.16c-.1-.1-.16-.25-.16-.43v-6.14c0-.19,.06-.35,.17-.47,.11-.12,.28-.18,.48-.18,.12,0,.23,.03,.33,.1,.1,.07,.19,.15,.25,.25l2.76,4.71v-4.48c0-.18,.05-.32,.16-.43Z"/><path class="cls-7" d="M305.72,50.13c.16,0,.29,.05,.39,.14,.1,.09,.15,.22,.15,.38s-.05,.29-.15,.38c-.1,.1-.23,.15-.39,.15h-2.73v1.89h2.41c.17,0,.3,.05,.4,.14,.1,.09,.15,.22,.15,.37s-.05,.3-.15,.39-.23,.14-.39,.14h-2.41v2.14h2.73c.17,0,.3,.05,.39,.14s.15,.22,.15,.38-.05,.3-.15,.39c-.1,.09-.23,.14-.39,.14h-3.34c-.19,0-.33-.05-.44-.16-.1-.11-.16-.25-.16-.43v-5.99c0-.18,.05-.32,.16-.43,.1-.11,.25-.16,.44-.16h3.34Z"/><path class="cls-7" d="M308.63,57.21c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19,.45,0,.83,.06,1.14,.18s.56,.28,.76,.46,.35,.39,.44,.61,.14,.43,.14,.63c0,.18-.05,.32-.14,.42-.09,.1-.22,.15-.39,.15-.16,0-.28-.04-.36-.11s-.14-.19-.18-.33c-.09-.35-.24-.59-.45-.74-.21-.15-.53-.22-.95-.22-.24,0-.45,.04-.63,.12-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92c0,.38,.03,.71,.09,.98,.06,.27,.16,.5,.29,.68,.13,.18,.28,.31,.47,.39,.18,.08,.39,.12,.63,.12s.45-.02,.61-.07c.16-.05,.3-.11,.41-.2,.11-.09,.19-.19,.25-.31,.06-.12,.11-.26,.14-.41,.04-.13,.1-.24,.18-.33s.2-.14,.35-.14c.17,0,.3,.05,.39,.14,.09,.09,.14,.23,.14,.4,0,.25-.05,.49-.16,.72-.1,.23-.26,.44-.46,.62-.2,.18-.46,.33-.77,.45-.31,.11-.67,.17-1.08,.17s-.76-.06-1.09-.19Z"/><path class="cls-7" d="M318.26,55.63c-.13,.41-.31,.74-.55,1-.23,.26-.51,.45-.85,.58-.33,.12-.7,.19-1.1,.19s-.76-.06-1.09-.19c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19s.77,.06,1.1,.19c.33,.12,.62,.32,.85,.58,.23,.26,.41,.59,.55,1,.13,.41,.2,.89,.2,1.46v.92c0,.56-.07,1.05-.2,1.46Zm-3.97-1.46c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12s.46-.04,.64-.12,.34-.21,.46-.38c.12-.18,.22-.4,.29-.68,.07-.27,.1-.6,.1-.99v-.92c0-.39-.03-.72-.1-.99-.07-.27-.16-.5-.29-.68-.12-.18-.28-.31-.46-.38s-.4-.12-.64-.12-.45,.04-.63,.12c-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92Z"/><path class="cls-7" d="M320.96,56.25h2.73c.16,0,.29,.05,.38,.15,.09,.1,.14,.23,.14,.38s-.05,.29-.14,.38-.22,.14-.38,.14h-3.34c-.19,0-.33-.05-.44-.16-.1-.11-.16-.25-.16-.43v-6.08c0-.18,.05-.32,.16-.43,.11-.11,.26-.16,.45-.16,.18,0,.32,.05,.43,.16,.11,.11,.16,.25,.16,.43v5.63Z"/><path class="cls-7" d="M329.68,55.63c-.13,.41-.31,.74-.55,1-.23,.26-.51,.45-.85,.58-.33,.12-.7,.19-1.1,.19s-.76-.06-1.09-.19c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19s.77,.06,1.1,.19c.33,.12,.62,.32,.85,.58,.23,.26,.41,.59,.55,1,.13,.41,.2,.89,.2,1.46v.92c0,.56-.07,1.05-.2,1.46Zm-3.97-1.46c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12s.46-.04,.64-.12,.34-.21,.46-.38c.12-.18,.22-.4,.29-.68,.07-.27,.1-.6,.1-.99v-.92c0-.39-.03-.72-.1-.99-.07-.27-.16-.5-.29-.68-.12-.18-.28-.31-.46-.38s-.4-.12-.64-.12-.45,.04-.63,.12c-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92Z"/><path class="cls-7" d="M332.47,57.21c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.86c0-.58,.06-1.07,.19-1.49s.31-.76,.55-1.02c.24-.26,.52-.46,.85-.58,.33-.12,.7-.19,1.09-.19,.42,0,.78,.06,1.1,.19,.32,.12,.58,.28,.79,.47,.21,.19,.36,.39,.47,.61s.16,.42,.16,.62c0,.18-.05,.32-.16,.42-.1,.1-.23,.15-.38,.15s-.28-.04-.37-.11-.16-.18-.21-.32c-.08-.3-.22-.54-.45-.71-.22-.17-.53-.25-.92-.25-.24,0-.46,.04-.64,.12-.19,.08-.34,.21-.47,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12,.22,0,.42-.03,.59-.1,.17-.07,.32-.17,.44-.31s.22-.31,.29-.53c.07-.21,.12-.46,.13-.74h-1.01c-.17,0-.3-.05-.4-.14-.1-.09-.15-.22-.15-.38s.05-.29,.15-.38c.1-.1,.23-.15,.4-.15h1.62c.18,0,.32,.05,.42,.16,.1,.1,.15,.24,.15,.42,0,.56-.06,1.04-.19,1.45s-.3,.73-.53,.99c-.23,.26-.51,.45-.83,.58-.33,.12-.69,.19-1.09,.19s-.76-.06-1.09-.19Z"/><path class="cls-7" d="M337.77,57.23c-.11-.11-.16-.26-.16-.44v-6.15c0-.18,.05-.33,.16-.44,.11-.11,.25-.17,.44-.17s.33,.06,.44,.17c.11,.11,.16,.26,.16,.44v6.15c0,.18-.05,.33-.16,.44-.11,.11-.25,.17-.44,.17s-.33-.06-.44-.17Z"/><path class="cls-7" d="M341.79,57.21c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19,.45,0,.83,.06,1.14,.18s.56,.28,.76,.46,.35,.39,.44,.61,.14,.43,.14,.63c0,.18-.05,.32-.14,.42-.09,.1-.22,.15-.39,.15-.16,0-.28-.04-.36-.11s-.14-.19-.18-.33c-.09-.35-.24-.59-.45-.74-.21-.15-.53-.22-.95-.22-.24,0-.45,.04-.63,.12-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92c0,.38,.03,.71,.09,.98,.06,.27,.16,.5,.29,.68,.13,.18,.28,.31,.47,.39,.18,.08,.39,.12,.63,.12s.45-.02,.61-.07c.16-.05,.3-.11,.41-.2,.11-.09,.19-.19,.25-.31,.06-.12,.11-.26,.14-.41,.04-.13,.1-.24,.18-.33s.2-.14,.35-.14c.17,0,.3,.05,.39,.14,.09,.09,.14,.23,.14,.4,0,.25-.05,.49-.16,.72-.1,.23-.26,.44-.46,.62-.2,.18-.46,.33-.77,.45-.31,.11-.67,.17-1.08,.17s-.76-.06-1.09-.19Z"/><path class="cls-7" d="M354.08,55.63c-.13,.41-.31,.74-.55,1-.23,.26-.51,.45-.85,.58-.33,.12-.7,.19-1.1,.19s-.76-.06-1.09-.19c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19s.77,.06,1.1,.19c.33,.12,.62,.32,.85,.58,.23,.26,.41,.59,.55,1,.13,.41,.2,.89,.2,1.46v.92c0,.56-.07,1.05-.2,1.46Zm-3.97-1.46c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12s.46-.04,.64-.12,.34-.21,.46-.38c.12-.18,.22-.4,.29-.68,.07-.27,.1-.6,.1-.99v-.92c0-.39-.03-.72-.1-.99-.07-.27-.16-.5-.29-.68-.12-.18-.28-.31-.46-.38s-.4-.12-.64-.12-.45,.04-.63,.12c-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92Z"/><path class="cls-7" d="M359.72,50.19c.1-.1,.25-.16,.43-.16s.32,.05,.43,.16c.1,.1,.16,.25,.16,.43v6.16c0,.19-.06,.34-.17,.45-.11,.11-.26,.17-.44,.17s-.31-.04-.41-.11-.18-.17-.24-.28l-2.74-4.67v4.48c0,.18-.05,.32-.16,.43-.11,.1-.25,.16-.43,.16s-.31-.05-.42-.16c-.1-.1-.16-.25-.16-.43v-6.14c0-.19,.06-.35,.17-.47,.11-.12,.28-.18,.48-.18,.12,0,.23,.03,.33,.1,.1,.07,.19,.15,.25,.25l2.76,4.71v-4.48c0-.18,.05-.32,.16-.43Z"/><path class="cls-7" d="M363.61,57.21c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19,.45,0,.83,.06,1.14,.18s.56,.28,.76,.46,.35,.39,.44,.61,.14,.43,.14,.63c0,.18-.05,.32-.14,.42-.09,.1-.22,.15-.39,.15-.16,0-.28-.04-.36-.11s-.14-.19-.18-.33c-.09-.35-.24-.59-.45-.74-.21-.15-.53-.22-.95-.22-.24,0-.45,.04-.63,.12-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92c0,.38,.03,.71,.09,.98,.06,.27,.16,.5,.29,.68,.13,.18,.28,.31,.47,.39,.18,.08,.39,.12,.63,.12s.45-.02,.61-.07c.16-.05,.3-.11,.41-.2,.11-.09,.19-.19,.25-.31,.06-.12,.11-.26,.14-.41,.04-.13,.1-.24,.18-.33s.2-.14,.35-.14c.17,0,.3,.05,.39,.14,.09,.09,.14,.23,.14,.4,0,.25-.05,.49-.16,.72-.1,.23-.26,.44-.46,.62-.2,.18-.46,.33-.77,.45-.31,.11-.67,.17-1.08,.17s-.76-.06-1.09-.19Z"/><path class="cls-7" d="M373.24,55.63c-.13,.41-.31,.74-.55,1-.23,.26-.51,.45-.85,.58-.33,.12-.7,.19-1.1,.19s-.76-.06-1.09-.19c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19s.77,.06,1.1,.19c.33,.12,.62,.32,.85,.58,.23,.26,.41,.59,.55,1,.13,.41,.2,.89,.2,1.46v.92c0,.56-.07,1.05-.2,1.46Zm-3.97-1.46c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12s.46-.04,.64-.12,.34-.21,.46-.38c.12-.18,.22-.4,.29-.68,.07-.27,.1-.6,.1-.99v-.92c0-.39-.03-.72-.1-.99-.07-.27-.16-.5-.29-.68-.12-.18-.28-.31-.46-.38s-.4-.12-.64-.12-.45,.04-.63,.12c-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92Z"/><path class="cls-7" d="M375.93,56.25h2.73c.16,0,.29,.05,.38,.15,.09,.1,.14,.23,.14,.38s-.05,.29-.14,.38-.22,.14-.38,.14h-3.34c-.19,0-.33-.05-.44-.16-.1-.11-.16-.25-.16-.43v-6.08c0-.18,.05-.32,.16-.43,.11-.11,.26-.16,.45-.16,.18,0,.32,.05,.43,.16,.11,.11,.16,.25,.16,.43v5.63Z"/><path class="cls-7" d="M384.66,55.63c-.13,.41-.31,.74-.55,1-.23,.26-.51,.45-.85,.58-.33,.12-.7,.19-1.1,.19s-.76-.06-1.09-.19c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.92c0-.57,.06-1.06,.19-1.46s.31-.74,.55-1c.24-.26,.52-.45,.85-.58,.33-.12,.7-.19,1.09-.19s.77,.06,1.1,.19c.33,.12,.62,.32,.85,.58,.23,.26,.41,.59,.55,1,.13,.41,.2,.89,.2,1.46v.92c0,.56-.07,1.05-.2,1.46Zm-3.97-1.46c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12s.46-.04,.64-.12,.34-.21,.46-.38c.12-.18,.22-.4,.29-.68,.07-.27,.1-.6,.1-.99v-.92c0-.39-.03-.72-.1-.99-.07-.27-.16-.5-.29-.68-.12-.18-.28-.31-.46-.38s-.4-.12-.64-.12-.45,.04-.63,.12c-.18,.08-.34,.21-.46,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92Z"/><path class="cls-7" d="M387.45,57.21c-.33-.12-.62-.32-.85-.58s-.42-.59-.55-1c-.13-.41-.19-.89-.19-1.46v-.86c0-.58,.06-1.07,.19-1.49s.31-.76,.55-1.02c.24-.26,.52-.46,.85-.58,.33-.12,.7-.19,1.09-.19,.42,0,.78,.06,1.1,.19,.32,.12,.58,.28,.79,.47,.21,.19,.36,.39,.47,.61s.16,.42,.16,.62c0,.18-.05,.32-.16,.42-.1,.1-.23,.15-.38,.15s-.28-.04-.37-.11-.16-.18-.21-.32c-.08-.3-.22-.54-.45-.71-.22-.17-.53-.25-.92-.25-.24,0-.46,.04-.64,.12-.19,.08-.34,.21-.47,.38-.12,.18-.22,.4-.29,.68-.07,.27-.1,.61-.1,.99v.92c0,.39,.03,.72,.1,.99,.07,.27,.16,.5,.29,.68,.12,.18,.28,.31,.46,.38,.18,.08,.39,.12,.63,.12,.22,0,.42-.03,.59-.1,.17-.07,.32-.17,.44-.31s.22-.31,.29-.53c.07-.21,.12-.46,.13-.74h-1.01c-.17,0-.3-.05-.4-.14-.1-.09-.15-.22-.15-.38s.05-.29,.15-.38c.1-.1,.23-.15,.4-.15h1.62c.18,0,.32,.05,.42,.16,.1,.1,.15,.24,.15,.42,0,.56-.06,1.04-.19,1.45s-.3,.73-.53,.99c-.23,.26-.51,.45-.83,.58-.33,.12-.69,.19-1.09,.19s-.76-.06-1.09-.19Z"/><path class="cls-7" d="M391.95,50.18c.12-.1,.27-.16,.45-.16,.12,0,.23,.03,.32,.08,.09,.05,.16,.13,.21,.22l1.45,2.67,1.42-2.64c.05-.1,.12-.18,.21-.24,.09-.07,.2-.1,.34-.1,.17,0,.32,.05,.44,.16,.12,.11,.18,.25,.18,.42,0,.06,0,.12-.03,.18-.02,.06-.04,.11-.07,.16l-1.89,3.37v2.5c0,.18-.06,.32-.17,.43s-.26,.16-.46,.16c-.18,0-.32-.05-.43-.16s-.16-.25-.16-.43v-2.5l-1.87-3.34s-.05-.1-.08-.16c-.02-.06-.04-.13-.04-.2,0-.18,.06-.32,.18-.43Z"/></g> </svg> </a> <div class="header-right d-none d-lg-block"> <div class="OA">Open Access, Peer-reviewed</div> <div class="follow"><a class="facebook" href="https://facebook.com/jgynoncol" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" height="30" viewBox="0 0 448 512"><path d="M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h137.25V327.69h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.27c-30.81 0-40.42 19.12-40.42 38.73V256h68.78l-11 71.69h-57.78V480H400a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48z"/></svg></a> <a class="instagram" href="https://www.instagram.com/jgo_journal" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" height="30" viewBox="0 0 448 512"><path d="M224.1 141c-63.6 0-114.9 51.3-114.9 114.9s51.3 114.9 114.9 114.9S339 319.5 339 255.9 287.7 141 224.1 141zm0 189.6c-41.1 0-74.7-33.5-74.7-74.7s33.5-74.7 74.7-74.7 74.7 33.5 74.7 74.7-33.6 74.7-74.7 74.7zm146.4-194.3c0 14.9-12 26.8-26.8 26.8-14.9 0-26.8-12-26.8-26.8s12-26.8 26.8-26.8 26.8 12 26.8 26.8zm76.1 27.2c-1.7-35.9-9.9-67.7-36.2-93.9-26.2-26.2-58-34.4-93.9-36.2-37-2.1-147.9-2.1-184.9 0-35.8 1.7-67.6 9.9-93.9 36.1s-34.4 58-36.2 93.9c-2.1 37-2.1 147.9 0 184.9 1.7 35.9 9.9 67.7 36.2 93.9s58 34.4 93.9 36.2c37 2.1 147.9 2.1 184.9 0 35.9-1.7 67.7-9.9 93.9-36.2 26.2-26.2 34.4-58 36.2-93.9 2.1-37 2.1-147.8 0-184.8zM398.8 388c-7.8 19.6-22.9 34.7-42.6 42.6-29.5 11.7-99.5 9-132.1 9s-102.7 2.6-132.1-9c-19.6-7.8-34.7-22.9-42.6-42.6-11.7-29.5-9-99.5-9-132.1s-2.6-102.7 9-132.1c7.8-19.6 22.9-34.7 42.6-42.6 29.5-11.7 99.5-9 132.1-9s102.7-2.6 132.1 9c19.6 7.8 34.7 22.9 42.6 42.6 11.7 29.5 9 99.5 9 132.1s2.7 102.7-9 132.1z"/></svg></a> <a class="twitter" href="https://twitter.com/jgo_journal" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" height="30" viewBox="0 0 448 512"><path d="M400 32H48C21.5 32 0 53.5 0 80v352c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48V80c0-26.5-21.5-48-48-48zm-48.9 158.8c.2 2.8.2 5.7.2 8.5 0 86.7-66 186.6-186.6 186.6-37.2 0-71.7-10.8-100.7-29.4 5.3.6 10.4.8 15.8.8 30.7 0 58.9-10.4 81.4-28-28.8-.6-53-19.5-61.3-45.5 10.1 1.5 19.2 1.5 29.6-1.2-30-6.1-52.5-32.5-52.5-64.4v-.8c8.7 4.9 18.9 7.9 29.6 8.3a65.447 65.447 0 0 1-29.2-54.6c0-12.2 3.2-23.4 8.9-33.1 32.3 39.8 80.8 65.8 135.2 68.6-9.3-44.5 24-80.6 64-80.6 18.9 0 35.9 7.9 47.9 20.7 14.8-2.8 29-8.3 41.6-15.8-4.9 15.2-15.2 28-28.8 36.1 13.2-1.4 26-5.1 37.8-10.2-8.9 13.1-20.1 24.7-32.9 34z"/></svg></a> <a class="podcast" href="https://podcasters.spotify.com/pod/show/ejgo" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" id="_레이어_2" data-name="레이어 2" height="24" viewBox="0 0 425.04 373.08"> <defs> <style> .cls-podcast1 { fill: #fff; } .cls-podcast1, .cls-podcast2 { fill-rule: evenodd; } .cls-podcast2 { fill: #1f9bd8; } </style> </defs> <g id="_레이어_1-2" data-name="레이어 1"> <g> <path class="cls-podcast2" d="m156.12,56.7c0-31.31,25.39-56.7,56.7-56.7s56.7,25.39,56.7,56.7-25.39,56.7-56.7,56.7-56.7-25.39-56.7-56.7h0Z"/> <path class="cls-podcast2" d="m127.8,232.02c0-31.31,38.06-56.7,85.02-56.7s85.02,25.39,85.02,56.7-38.06,56.7-85.02,56.7-85.02-25.39-85.02-56.7h0Z"/> <path class="cls-podcast1" d="m141.96,207.54c0-31.31,31.73-56.7,70.86-56.7s70.86,25.39,70.86,56.7-31.73,56.7-70.86,56.7-70.86-25.39-70.86-56.7h0Z"/> <polygon class="cls-podcast2" points="156.12 52.92 269.52 52.92 269.52 194.64 156.12 194.64 156.12 52.92 156.12 52.92"/> <path class="cls-podcast2" d="m156.12,194.7c0-31.31,25.39-56.7,56.7-56.7s56.7,25.39,56.7,56.7-25.39,56.7-56.7,56.7-56.7-25.39-56.7-56.7h0Z"/> <polygon class="cls-podcast1" points="127.8 162 297.84 162 297.84 180.6 127.8 180.6 127.8 162 127.8 162"/> <polygon class="cls-podcast2" points="202.32 274.92 223.2 274.92 223.2 316.08 202.32 316.08 202.32 274.92 202.32 274.92"/> <path class="cls-podcast2" d="m99.36,344.76c0-15.64,50.77-28.32,113.4-28.32s113.4,12.68,113.4,28.32-50.77,28.32-113.4,28.32-113.4-12.68-113.4-28.32h0Z"/> <polygon class="cls-podcast1" points="133.2 40.08 303.24 40.08 303.24 58.8 133.2 58.8 133.2 40.08 133.2 40.08"/> <path class="cls-podcast2" d="m324.6,85.68c30.55,0,55.32,31.73,55.32,70.86s-24.77,70.86-55.32,70.86v-27.66c15.28,0,27.66-19.34,27.66-43.2s-12.38-43.2-27.66-43.2v-27.66Z"/> <path class="cls-podcast2" d="m369.72,43.2c30.55,0,55.32,50.77,55.32,113.4s-24.77,113.4-55.32,113.4v-27.66c15.28,0,27.66-38.39,27.66-85.74s-12.38-85.74-27.66-85.74v-27.66Z"/> <path class="cls-podcast2" d="m99.72,85.68c-30.55,0-55.32,31.73-55.32,70.86s24.77,70.86,55.32,70.86v-27.66c-15.28,0-27.66-19.34-27.66-43.2s12.38-43.2,27.66-43.2v-27.66Z"/> <path class="cls-podcast2" d="m55.32,43.2C24.77,43.2,0,93.97,0,156.6s24.77,113.4,55.32,113.4v-27.66c-15.28,0-27.66-38.39-27.66-85.74s12.38-85.74,27.66-85.74v-27.66Z"/> </g> </g> </svg> </a></div> <div class="issn">pISSN 2005-0380   eISSN 2005-0399 </div> </div> </div> <div class="container"> <nav class="navbar navbar-expand-lg"> <div class="container-fluid"> <button class="navbar-toggler" type="button" data-bs-toggle="offcanvas" data-bs-target="#offcanvasNavbar" aria-controls="offcanvasNavbar" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="navbar-toggler p-0"> <button class="btn" type="button" data-bs-toggle="collapse" href="#navbar-search-collapse" role="button" aria-expanded="false" aria-controls="navbar-search-collapse">  <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-search" viewBox="0 0 16 16"> <path d="M11.742 10.344a6.5 6.5 0 1 0-1.397 1.398h-.001c.03.04.062.078.098.115l3.85 3.85a1 1 0 0 0 1.415-1.414l-3.85-3.85a1.007 1.007 0 0 0-.115-.1zM12 6.5a5.5 5.5 0 1 1-11 0 5.5 5.5 0 0 1 11 0z"></path> </svg> </button> </div> <div class="collapse navbar-collapse"> <ul class="nav me-auto mb-2 mb-lg-0"> <li class="nav-item"> <a class="nav-link active" aria-current="page" href="/">Home</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" role="button" data-bs-toggle="dropdown" aria-expanded="false">About</a> <ul class="dropdown-menu"> <li><a class="dropdown-item" href="/index.php?body=aims">Aims and Scope</a></li> <li><a class="dropdown-item" href="/index.php?body=board">Editorial Board</a></li> <li><a class="dropdown-item" href="/index.php?body=j-info">Journal Information</a></li> <li><a class="dropdown-item" href="/index.php?body=notice">JGO News</a></li> <li><a class="dropdown-item" href="/index.php?body=reviewer">Best Reviewers</a></li> <li><a class="dropdown-item" href="/index.php?body=honorsnawards">Honors & Awards</a></li> <li><a class="dropdown-item" href="/index.php?body=webinar">ASGO Webinar</a></li> </ul> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" role="button" data-bs-toggle="dropdown" aria-expanded="false">Browse</a> <ul class="dropdown-menu"> <li><a class="dropdown-item" href="/index.php?body=forthcoming">Online First Articles</a></li> <li><a class="dropdown-item" href="/index.php?body=current">Current Issue</a></li> <li><a class="dropdown-item" href="/index.php?body=archive">All Issues</a></li> <li><a class="dropdown-item" href="/index.php?body=video-articles">Articles by Categories</a></li> <li><a class="dropdown-item" href="/index.php?body=editor-choice">Editor's Choice</a></li> <li><a class="dropdown-item" href="/index.php?body=most-read-PMC">Most Read: PMC</a></li> <li><a class="dropdown-item" href="/index.php?body=most-read-Crossref">Most Read: DOI</a></li> <li><a class="dropdown-item" href="/index.php?body=most-cited">Most Cited: WoS</a></li> </ul> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" role="button" data-bs-toggle="dropdown" aria-expanded="false">Author Center</a> <ul class="dropdown-menu"> <li><a class="dropdown-item" href="/index.php?body=instructions">Instructions for authors</a></li> <li><a class="dropdown-item" href="http://www.editorialmanager.com/jgynecoloncol/default.aspx" target="_black"><img src="images/button-esubmission.png" alt=""></a></li> <li><a class="dropdown-item" href="/index.php?body=openaccess">About Open Access</a></li> </ul> </li> </ul> <button class="btn" type="button" data-bs-toggle="collapse" href="#navbar-search-collapse" role="button" aria-expanded="false" aria-controls="navbar-search-collapse">  <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-search" viewBox="0 0 16 16"> <path d="M11.742 10.344a6.5 6.5 0 1 0-1.397 1.398h-.001c.03.04.062.078.098.115l3.85 3.85a1 1 0 0 0 1.415-1.414l-3.85-3.85a1.007 1.007 0 0 0-.115-.1zM12 6.5a5.5 5.5 0 1 1-11 0 5.5 5.5 0 0 1 11 0z"></path> </svg> </button> </div> <div class="collapse offcanvas offcanvas-start" tabindex="-1" id="offcanvasNavbar" aria-labelledby="offcanvasNavbarLabel"> <div class="offcanvas-header border-bottom"> <h1 class="offcanvas-title" id="offcanvasNavbarLabel">Journal of Gynecologic Oncology</h1> <button type="button" class="btn-close" data-bs-dismiss="offcanvas" aria-label="Close"></button> </div> <div class="offcanvas-body"> <ul class="navbar-nav justify-content-end flex-grow-1 pe-3"> <li class="nav-item"><a href="/">Home</a></li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" role="button" data-bs-toggle="dropdown" aria-expanded="false">About</a> <ul class="dropdown-menu"> <li><a class="dropdown-item" href="/index.php?body=aims">Aims and Scope</a></li> <li><a class="dropdown-item" href="/index.php?body=board">Editorial Board</a></li> <li><a class="dropdown-item" href="/index.php?body=j-info">Journal Information</a></li> <li><a class="dropdown-item" href="/index.php?body=notice">JGO News</a></li> <li><a class="dropdown-item" href="/index.php?body=reviewer">Best Reviewers</a></li> <li><a class="dropdown-item" href="/index.php?body=honorsnawards">Honors & Awards</a></li> <li><a class="dropdown-item" href="/index.php?body=webinar">ASGO Webinar</a></li> </ul> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" role="button" data-bs-toggle="dropdown" aria-expanded="false">Browse</a> <ul class="dropdown-menu"> <li><a class="dropdown-item" href="/index.php?body=forthcoming">Online First Articles</a></li> <li><a class="dropdown-item" href="/index.php?body=current">Current Issue</a></li> <li><a class="dropdown-item" href="/index.php?body=archive">All Issues</a></li> <li><a class="dropdown-item" href="/index.php?body=video-articles">Articles by Categories</a></li> <li><a class="dropdown-item" href="/index.php?body=editor-choice">Editor's Choice</a></li> <li><a class="dropdown-item" href="/index.php?body=most-read">Most Read: PMC</a></li> <li><a class="dropdown-item" href="/index.php?body=most-read">Most Read: DOI</a></li> <li><a class="dropdown-item" href="/index.php?body=most-cited">Most Cited: WoS</a></li> </ul> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" role="button" data-bs-toggle="dropdown" aria-expanded="false">Author Center</a> <ul class="dropdown-menu"> <li><a class="dropdown-item" href="/index.php?body=instructions">Instructions for authors</a></li> <li><a class="dropdown-item" href="http://www.editorialmanager.com/jgynecoloncol/default.aspx" target="_black"><img src="images/button-esubmission.png" alt=""></a></li> <li><a class="dropdown-item" href="/index.php?body=openaccess">About Open Access</a></li> </ul> </li> <li class="bd-links-span-all my-3 border-top"></li> <li class="nav-item dropdown"></li> </ul> </div> </div> </div> </nav> <div class="collapse" id="navbar-search-collapse"> <div class="card border border-0 rounded-0"> <div class="card-body"> <form action="search.php" method="get" id="FormBasicSearch" class="d-flex" role="search"> <div class="input-group"> <input type="hidden" name="where" value="asummary"> <input type="text" name="query" class="form-control" placeholder="Enter keyword, author, title or citation" aria-label="Enter keyword, author, title or citation" aria-describedby="button-addon2"> <button class="searchbar__submit-btn" aria-label="search" title="search" type="submit" data-gsearch-submit=""> <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-search" viewBox="0 0 16 16"> <path d="M11.742 10.344a6.5 6.5 0 1 0-1.397 1.398h-.001c.03.04.062.078.098.115l3.85 3.85a1 1 0 0 0 1.415-1.414l-3.85-3.85a1.007 1.007 0 0 0-.115-.1zM12 6.5a5.5 5.5 0 1 1-11 0 5.5 5.5 0 0 1 11 0z"></path></svg> </button> <a href="/AdvSearchBlade.php" class="searchbar__advanced-search-link mx-2 ms-4" arialabelledby="advanced search" title="advanced search">Advanced Search</a> </div> </form> </div> </div> </div> </div> </div> </div> <div class="line"></div>  <div id="navbar-article-top-toolbar" class="sticky-top d-lg-none"> <div class="container text-center py-1"> <div class="row justify-content-start"> <div class="col dropdown"> <a class="dropdown-toggle nav-link text-white" href="#" id="navbar-article-top-toolbar-navbarDropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> <i class="bi bi-list-ul text-white"></i> </a> <ul id="navbar-article-top-toolbar-goto" class="dropdown-menu w-100" aria-labelledby="navbar-article-top-toolbar-navbarDropdown"></ul> </div> <div class="col"><a id="article-move-supplement-material" href="#supplementary-material" class="nav-link"><i class="bi bi-paperclip text-white"></i></a></div> <div class="col"><a href="/pdf/10.3802/jgo.2025.36.e26" class="nav-link" onclick="trackDownloadLink('PDF', '10.3802/jgo.2025.36.e26');"><i class="bi bi-file-earmark-pdf text-white"></i></a></div> <div class="col"><a href="#" class="nav-link CiteDialog" data-article-id=10.3802/jgo.2025.36.e26 data-article-citation=WmhhbyBNLCBMaSBaLCBHdSBYLCBZYW5nIFgsIEdhbyBaLCBXYW5nIFMsIEZ1IEouIFRoZSByb2xlIG9mIHJhZGlvbWljcyBmb3IgcHJlZGljdGluZyBvZiBseW1waC12YXNjdWxhciBzcGFjZSBpbnZhc2lvbiBpbiBjZXJ2aWNhbCBjYW5jZXIgcGF0aWVudHMgYmFzZWQgb24gYXJ0aWZpY2lhbCBpbnRlbGxpZ2VuY2U6IGEgc3lzdGVtYXRpYyByZXZpZXcgYW5kIG1ldGEtYW5hbHlzaXMuIEogR3luZWNvbCBPbmNvbC4gMjAyNCBKdWw7MzY6ZTI2LiA8QSBIUkVGPSJodHRwczovL2RvaS5vcmcvMTAuMzgwMi9qZ28uMjAyNS4zNi5lMjYiIHRhcmdldD1fYmxhbms+aHR0cHM6Ly9kb2kub3JnLzEwLjM4MDIvamdvLjIwMjUuMzYuZTI2PC9BPg==><i class="bi bi-quote text-white"></i></a></div> <div class="col"><a href="#" class="nav-link"><i class="bi bi-chevron-up text-white"></i></a></div> </div> </div> </div> </header> <main> <div class="container"> <nav> <ol class="breadcrumb ms-1" > <li class="breadcrumb-item"><a href="/">Home</a></li> <li class="breadcrumb-item"><a href="/index.php?body=archive">Archive</a></li> <li class="breadcrumb-item"><a href="search.php?where=asummary&id=17005&code=1114JGO">v.36; 2025</a></li> <li class="breadcrumb-item active" aria-current="page">10.3802/jgo.2025.36.e26</li> </ol> </nav> <hr> <section> <div class="row"> <div class="col-xxl-8 col-xl-9 col-lg-8"> <div xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:php="http://php.net/xsl" class="abody"> <a id="top" name="top"></a> <div id="article-level-0-front" class="fm"> <div><div id="article-front-meta" class="row no-gutters my-3"> <div class="d-flex"> <div id="article-front-meta-left" class="w-100"> <div>J Gynecol Oncol. 2025;36:e26. <span class="article-front-meta-type">Forthcoming.</span> English.<br>Published online Jul 19, 2024.<br><a class="article-meta-doi-link" href="https://doi.org/10.3802/jgo.2025.36.e26">https://doi.org/10.3802/jgo.2025.36.e26</a> </div> <div>© 2025. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology, and Japan Society of Gynecologic Oncology </div> </div> <div id="article-front-meta-right" class="flex-shrink-1"><div><div id="crossref-crossmark" style="display:none;"><a data-target="crossmark"></a></div></div></div> </div> <div class="clearfix"></div> </div></div> <div id="article-front-bibliographic"> <div class="article-type">Review</div> <div class="article-title"><h1 class="content-title">The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis </h1></div> <div class="article-contrib"> <span class="capture-id"><span id="ORCID_0000-0001-6337-7516" class="ORCID"><a href="ORCID/0000-0001-6337-7516">Mengli Zhao</a></span><a href="https://orcid.org/0000-0001-6337-7516" target="_blank"><img style="vertical-align:middle; margin-left:0.2em; margin-right:0.05em; padding-bottom: 0.5em;" src="image/icon-orcid.jpg"></a>,<sup>1</sup><sup>,</sup><sup>*</sup></span> <span class="capture-id"><span id="ORCID_0000-0001-9395-8004" class="ORCID"><a href="ORCID/0000-0001-9395-8004">Zhen Li</a></span><a href="https://orcid.org/0000-0001-9395-8004" target="_blank"><img style="vertical-align:middle; margin-left:0.2em; margin-right:0.05em; padding-bottom: 0.5em;" src="image/icon-orcid.jpg"></a>,<sup>2</sup><sup>,</sup><sup>*</sup></span> <span class="capture-id"><span id="ORCID_0000-0001-8501-5305" class="ORCID"><a href="ORCID/0000-0001-8501-5305">Xiaowei Gu</a></span><a href="https://orcid.org/0000-0001-8501-5305" target="_blank"><img style="vertical-align:middle; margin-left:0.2em; margin-right:0.05em; padding-bottom: 0.5em;" src="image/icon-orcid.jpg"></a>,<sup>3</sup></span> <span class="capture-id"><span id="ORCID_0000-0003-4881-9771" class="ORCID"><a href="ORCID/0000-0003-4881-9771">Xiaojing Yang</a></span><a href="https://orcid.org/0000-0003-4881-9771" target="_blank"><img style="vertical-align:middle; margin-left:0.2em; margin-right:0.05em; padding-bottom: 0.5em;" src="image/icon-orcid.jpg"></a>,<sup>1</sup></span> <span class="capture-id"><span id="ORCID_0009-0000-0312-3418" class="ORCID"><a href="ORCID/0009-0000-0312-3418">Zhongrong Gao</a></span><a href="https://orcid.org/0009-0000-0312-3418" target="_blank"><img style="vertical-align:middle; margin-left:0.2em; margin-right:0.05em; padding-bottom: 0.5em;" src="image/icon-orcid.jpg"></a>,<sup>1</sup></span> <span class="capture-id"><span id="ORCID_0000-0002-1790-3359" class="ORCID"><a href="ORCID/0000-0002-1790-3359">Shanshan Wang</a></span><a href="https://orcid.org/0000-0002-1790-3359" target="_blank"><img style="vertical-align:middle; margin-left:0.2em; margin-right:0.05em; padding-bottom: 0.5em;" src="image/icon-orcid.jpg"></a>,<sup>1</sup></span> and <span class="capture-id"><span id="ORCID_0000-0002-3183-453X" class="ORCID"><a href="ORCID/0000-0002-3183-453X">Jie Fu</a></span><a href="https://orcid.org/0000-0002-3183-453X" target="_blank"><img style="vertical-align:middle; margin-left:0.2em; margin-right:0.05em; padding-bottom: 0.5em;" src="image/icon-orcid.jpg"></a><span class="article-icon-corresp"></span><sup>1</sup></span> </div> <div class="article-front-meta-sub-tab mt-3 mb-0"><div class="d-inline-flex flex-wrap"> <div class=""><a class="btn btn-outline-article" data-bs-toggle="collapse" href="#article-front-meta-id-aurthorinfo" role="button" aria-expanded="false" aria-controls="article-front-meta-id-aurthorinfo">Author information</a></div> <div class=""><a class="btn btn-outline-article" data-bs-toggle="collapse" href="#article-front-meta-id-articlehistory" role="button" aria-expanded="false" aria-controls="article-front-meta-id-articlehistory">Author notes</a></div> <div class=""><a class="btn btn-outline-article" data-bs-toggle="collapse" href="#article-front-meta-id-license" role="button" aria-expanded="false" aria-controls="article-front-meta-id-license">Copyright and License</a></div> </div></div> <div class="article-front-meta-sub"> <div class="collapse multi-collapse" id="article-front-meta-id-aurthorinfo"><div class="card card-body"><ul class="list-group list-group-flush"> <li class="list-group-item"><div class="article-afilliations"><ul> <li> <sup>1</sup>Department of Radiation Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.<br> </li> <li> <sup>2</sup>ENT institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.<br> </li> <li> <sup>3</sup>Department of Radiation Oncology, Jiangyin Hospital Affiliated to Nantong University, Jiangyin, China.<br> </li> </ul></div></li> <li class="list-group-item"><div class="article-author-notes"><span class="capture-id"><div> <span class="gen"></span><span class="article-icon-corresp"></span>Correspondence to Jie Fu. Department of Radiation Oncology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 227 South Chongqing Road, Shanghai 200233, China. <span class="author-email">Email: <A href="mailto:fujie74@sjtu.edu.cn">fujie74@sjtu.edu.cn</A></span> . </div> <div class="fm-footnote" id="FN1"><div class="p p-first"><span class="capture-id"><p></p> <sup>*</sup>Mengli Zhao and Zhen Li contributed equally to this study.</span></div></div></span></div></li> </ul></div></div> <div class="collapse multi-collapse" id="article-front-meta-id-articlehistory"><div class="card card-body"><div class="article-received-accepted-date">Received January 16, 2024; Revised June 17, 2024; Accepted July 07, 2024.</div></div></div> <div class="collapse multi-collapse" id="article-front-meta-id-license"><div class="card card-body"><div class="article-license"> <p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_blank">https://creativecommons.org/licenses/by-<wbr/>nc/4.0/</a>) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> </div></div></div> <div id="article-citedby"></div> <div id="article-relate-articles"></div> </div> <div><div class="article-abstract"> <div class="article-section-header"> <div class="__SECTION__" id="__ID_SECTION_0" data-section-name="Abstract"><div class="dropdown"> <a href="#" class="dropdown-goto dropdown-toggle d-none" id="dropdownGoto_0" title="Go to other sections in this page" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Go to:</a><div class="dropdown-menu dropdown-menu-right d-none" aria-labelledby="dropdownGoto_0"></div> </div></div> <h2>Abstract</h2> </div> <p>The primary aim of this study was to conduct a methodical examination and assessment of the prognostic efficacy exhibited by magnetic resonance imaging (MRI)-derived radiomic models concerning the preoperative prediction of lymph-vascular space infiltration (LVSI) in cervical cancer cases. A comprehensive and thorough exploration of pertinent academic literature was undertaken by two investigators, employing the resources of the Embase, PubMed, Web of Science, and Cochrane Library databases. The scope of this research was bounded by a publication cutoff date of May 15, 2023. The inclusion criteria encompassed studies that utilized radiomic models based on MRI to prognosticate the accuracy of preoperative LVSI estimation in instances of cervical cancer. The Diagnostic Accuracy Studies-2 framework and the Radiomic Quality Score metric were employed. This investigation included nine distinct research studies, enrolling a total of 1,406 patients. The diagnostic performance metrics of MRI-based radiomic models in the prediction of preoperative LVSI among cervical cancer patients were determined as follows: sensitivity of 83% (95% confidence interval [CI]=77%–87%), specificity of 74% (95% CI=69%–79%), and a corresponding AUC of summary receiver operating characteristic measuring 0.86 (95% CI=0.82–0.88). The results of the synthesized meta-analysis did not reveal substantial heterogeneity.This meta-analysis suggests the robust diagnostic proficiency of the MRI-based radiomic model in the prognostication of preoperative LVSI within the cohort of cervical cancer patients. In the future, radiomics holds the potential to emerge as a widely applicable noninvasive modality for the early detection of LVSI in the context of cervical cancer.</p> </div></div> </div> </div> <div id="article-level-0-end-metadata" class="fm"> <div class="my-3"><div> <div class="article-keyword-group-title">Keywords</div> <div> <span class="capture-id">Cervical Cancer</span>; <span class="capture-id">Radiomics</span>; <span class="capture-id">Deep Learning</span>; <span class="capture-id">Artificial Intelligence</span>; <span class="capture-id">Magnetic Resonance Imaging</span>; <span class="capture-id">Lymph-Vascular Space Infiltration</span> </div> </div></div> </div> <div id="article-level-0-body" class="body"> <div> <div><div class="article-section-header"> <div class="__SECTION__" id="__ID_SECTION_1" data-section-name="INTRODUCTION"><div class="dropdown"> <a href="#" class="dropdown-goto dropdown-toggle d-none" id="dropdownGoto_1" title="Go to other sections in this page" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Go to:</a><div class="dropdown-menu dropdown-menu-right d-none" aria-labelledby="dropdownGoto_1"></div> </div></div> <h2>INTRODUCTION</h2> </div></div> <p>Cervical cancer (CC) endures as the fourth most prevalent among female malignancies. Research records approximately 340,000 documented fatalities and 600,000 reported new cases worldwide in 2020 [<span class="xref"><span id="XREF_B1" class="ref-destination-back"></span><a href="#B1" data-id="XREF_B1" data-toggle="ref-popover" data-trigger="manual" data-placement="right">1</a></span>]. Studies have indicated that radiation therapy yields comparable efficacy to surgery in patients with early CC. Moreover, the radiotherapy cohort exhibits a notably diminished incidence of severe morbidity in comparison to the surgical cohort (12%–23% vs. 28%–32%) [<span class="xref"><span id="XREF_B2" class="ref-destination-back"></span><a href="#B2" data-id="XREF_B2" data-toggle="ref-popover" data-trigger="manual" data-placement="right">2</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B3" class="ref-destination-back"></span><a href="#B3" data-id="XREF_B3" data-toggle="ref-popover" data-trigger="manual" data-placement="right">3</a></span>]. The National Comprehensive Cancer Network (NCCN) guidelines underscore the necessity of adjuvant radiotherapy for CC patients with established risk factors post-surgery [<span class="xref"><span id="XREF_B4" class="ref-destination-back"></span><a href="#B4" data-id="XREF_B4" data-toggle="ref-popover" data-trigger="manual" data-placement="right">4</a></span>]. However, the concurrent application of radiotherapy and surgical intervention escalates the likelihood of complications [<span class="xref"><span id="XREF_B2" class="ref-destination-back"></span><a href="#B2" data-id="XREF_B2" data-toggle="ref-popover" data-trigger="manual" data-placement="right">2</a></span>]. As a result, precise identification of adverse prognostic factors before surgical intervention emerges as a pivotal imperative for optimizing therapeutic strategies and curtailing the occurrence of complications.</p> <p>Lymph-vascular space infiltration (LVSI) designates the dissemination of neoplastic cells within lymphatic and/or blood vessels, serving as an autonomous prognostic factor in the outcome of CC cases, particularly those exhibiting negative lymph node status [<span class="xref"><span id="XREF_B5" class="ref-destination-back"></span><a href="#B5" data-id="XREF_B5" data-toggle="ref-popover" data-trigger="manual" data-placement="right">5</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B6" class="ref-destination-back"></span><a href="#B6" data-id="XREF_B6" data-toggle="ref-popover" data-trigger="manual" data-placement="right">6</a></span>]. Empirical investigations have evidenced a 47% risk reduction in recurrence rates among CC patients with LVSI upon the administration of adjuvant radiotherapy [<span class="xref"><span id="XREF_B7" class="ref-destination-back"></span><a href="#B7" data-id="XREF_B7" data-toggle="ref-popover" data-trigger="manual" data-placement="right">7</a></span>]. Despite the array of methodologies available for LVSI detection, including conical biopsy, cervical biopsy, and conventional imaging modalities, these approaches fail to attain the desired accuracy. Presently, LVSI prognostication predominantly hinges upon the histopathological analysis of hysterectomy specimens post-operatively, thereby highlighting the absence of effective non-invasive diagnostic techniques for the preoperative assessment of LVSI [<span class="xref"><span id="XREF_B8" class="ref-destination-back"></span><a href="#B8" data-id="XREF_B8" data-toggle="ref-popover" data-trigger="manual" data-placement="right">8</a></span>]. Given the elevated incidence of complications associated with combined therapeutic regimens, precise preoperative LVSI prediction emerges as a pivotal necessity.</p> <p>Medical images contain valuable information that could be effectively utilized through computer-assisted interpretation, which is radiomics. The objective is to extract quantitative data from medical images for utilization as clinical decision support tools [<span class="xref"><span id="XREF_B9" class="ref-destination-back"></span><a href="#B9" data-id="XREF_B9" data-toggle="ref-popover" data-trigger="manual" data-placement="right">9</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B10" class="ref-destination-back"></span><a href="#B10" data-id="XREF_B10" data-toggle="ref-popover" data-trigger="manual" data-placement="right">10</a></span>]. Radiomics emerges as a novel technique within medical artificial intelligence, represents an expeditiously evolving discipline that combines facets of artificial intelligence, imaging science, and oncology, effectively bridging the domains of computer engineering and medicine practice, capable of garnering heightened tumor-specific insights from medical images by means of quantitative feature extraction, transformation, and analysis [<span class="xref"><span id="XREF_B11" class="ref-destination-back"></span><a href="#B11" data-id="XREF_B11" data-toggle="ref-popover" data-trigger="manual" data-placement="right">11</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B12" class="ref-destination-back"></span><a href="#B12" data-id="XREF_B12" data-toggle="ref-popover" data-trigger="manual" data-placement="right">12</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B13" class="ref-destination-back"></span><a href="#B13" data-id="XREF_B13" data-toggle="ref-popover" data-trigger="manual" data-placement="right">13</a></span>]. Radiomics possesses the capability to unveil heterogeneity nuances amongst lesions that often elude visual differentiation, effectively circumventing the limitations inherent to rudimentary visual representations. A burgeoning corpus of research underscores radiomics’ pivotal role in diagnosis, prognosis, and therapeutic delineations within the oncological spectrum, signifying it as a read-hot research avenue that integrates artificial intelligence and medical science both presently and in future trajectories [<span class="xref"><span id="XREF_B14" class="ref-destination-back"></span><a href="#B14" data-id="XREF_B14" data-toggle="ref-popover" data-trigger="manual" data-placement="right">14</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B15" class="ref-destination-back"></span><a href="#B15" data-id="XREF_B15" data-toggle="ref-popover" data-trigger="manual" data-placement="right">15</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B16" class="ref-destination-back"></span><a href="#B16" data-id="XREF_B16" data-toggle="ref-popover" data-trigger="manual" data-placement="right">16</a></span>]. In recent years, the evolution of radiomics has exerted a notable influence on the development of precision medicine, facilitating the non-invasive diagnosis of a variety of conditions. Research conducted by Wu et al. [<span class="xref"><span id="XREF_B17" class="ref-destination-back"></span><a href="#B17" data-id="XREF_B17" data-toggle="ref-popover" data-trigger="manual" data-placement="right">17</a></span>] has demonstrated the efficacy of a deep learning model anchored in magnetic resonance imaging (MRI) for the accurate preoperative prediction of lymph node status in CC patients. Analogous findings have been documented in the context of breast and rectal malignancies as well [<span class="xref"><span id="XREF_B18" class="ref-destination-back"></span><a href="#B18" data-id="XREF_B18" data-toggle="ref-popover" data-trigger="manual" data-placement="right">18</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B19" class="ref-destination-back"></span><a href="#B19" data-id="XREF_B19" data-toggle="ref-popover" data-trigger="manual" data-placement="right">19</a></span>]. Liu et al. have articulated that predictive models rooted in radiomic analysis proficiently forecast LVSI within the domain of breast cancer [<span class="xref"><span id="XREF_B20" class="ref-destination-back"></span><a href="#B20" data-id="XREF_B20" data-toggle="ref-popover" data-trigger="manual" data-placement="right">20</a></span>]. An increasing number of scholars are directing their attention towards the prospective utility of radiomics in the preoperative prediction of LVSI within CC. Given the prevailing issues of limited sample sizes and single-center focus in a majority of investigations, this study endeavors to further establish the diagnostic efficacy of MRI-derived radiomics for the preoperative prediction of LVSI among CC patients, employing a systematic review and meta-analysis approach.</p> </div> <div> <div><div class="article-section-header"> <div class="__SECTION__" id="__ID_SECTION_2" data-section-name="MATERIAL AND METHODS"><div class="dropdown"> <a href="#" class="dropdown-goto dropdown-toggle d-none" id="dropdownGoto_2" title="Go to other sections in this page" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Go to:</a><div class="dropdown-menu dropdown-menu-right d-none" aria-labelledby="dropdownGoto_2"></div> </div></div> <h2>MATERIAL AND METHODS</h2> </div></div> <div id="sec1"> <p class="article-section-sub-title article-section-sub-title-level-2"><h3>1. Research search</h3></p> <p>A systematic exploration of the literature was undertaken across prominent databases including PubMed, Embase, Web of Science, and Cochrane Library, encompassing English-language publications prior to May 15, 2023. The search strategy incorporated the subsequent search terms and their synonymous iterations: (“Uterine Cervical Neoplasms” OR “Cervical Cancer” OR “Uterine Cervical Cancer” OR “Cervical Carcinoma”) AND (“Magnetic Resonance Imaging” OR “MRI” OR “MR”) AND (“artificial intelligence” OR “radiomic” OR “deep learning” OR “machine learning”) (<span class="fig-table-link"><a href="#S1">Data S1</a></span>). The process of literature inquiry and filtration was undertaken independently by 2 investigators. In the initial phase, a preliminary evaluation of all identified literature was conducted based on the title and abstract content, adhering to the PICO principle. Subsequently, the second phase encompassed meticulous scrutiny of the complete texts in alignment with the pre-established inclusion and exclusion criteria. The engagement of a third investigator was instituted to facilitate discourse and resolution for any divergences encountered throughout the screening trajectory.</p> </div> <div id="sec2"> <p class="article-section-sub-title article-section-sub-title-level-2"><h3>2. The criteria of inclusion and exclusion</h3></p> <p>The criteria for inclusion criteria were as follows: 1) Patients included in the study were required to have confirmed diagnoses of CC through pathological assessment, with the LVSI status similarly determined through pathology; 2) The study focused on evaluating the potential of MRI-based radiomics, deep learning, or artificial intelligence techniques for preoperative assessment of LVSI in CC; 3) Only original studies published in English were considered. On the other hand, exclusion criteria consisted of: 1) Studies involving animals; 2) Non-original and informal research types such as case reports, reviews, and conference abstracts; 3) Patients who had undergone chemotherapy, radiation, or surgery prior to MRI evaluation; 4) Studies utilizing imaging omics analysis other than MRI, such as computed tomography (CT) or positron emission tomography (PET)-CT/MRI; 5) Studies exclusively centered on imaging methodology without subsequent evaluation of its practical application.</p> </div> <div id="sec3"> <p class="article-section-sub-title article-section-sub-title-level-2"><h3>3. Data extraction</h3></p> <p>After completing the literature screening, two investigators began autonomously conducted the extraction of data from the included studies on June 8, 2023. The extracted information encompassed various aspects, comprising the first author’s identity, publication date, MRI machine specifications including type and sequence, overall patient cohort size, patient distribution across distinct groups, specifics associated with segmentation, the procedure employed for the selection and extraction of image features, the algorithm employed for radiomic analysis, the stage classification according to the International Federation of Gynecology and Obstetrics (FIGO), LVSI prevalence, and the essential parameters, namely true positive, false positive, true negative, and false negative outcomes, for each individual study that met the inclusion criteria.</p> </div> <div id="sec4"> <p class="article-section-sub-title article-section-sub-title-level-2"><h3>4. Quality assessment</h3></p> <p>The methodological rigor and inherent risk of bias within the included studies were assessed in an independent manner by the 2 investigators, employing the Diagnostic Accuracy Studies-2 (QUADAS-2) framework and Radiomics Quality Score (RQS) criteria [<span class="xref"><span id="XREF_B13" class="ref-destination-back"></span><a href="#B13" data-id="XREF_B13" data-toggle="ref-popover" data-trigger="manual" data-placement="right">13</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B21" class="ref-destination-back"></span><a href="#B21" data-id="XREF_B21" data-toggle="ref-popover" data-trigger="manual" data-placement="right">21</a></span>]. This QUADAS-2 framework involves the categorization of each component as “high,” “low,” or “uncertain risk,” predicted on responses to pertinent inquiries. And the RQS criteria scores each study according to different criteria, with a maximum score of 36. Instances of discordance encountered during the evaluation process were addressed through deliberation and resolution, facilitated by the involvement of a third investigator.</p> </div> <div id="sec5"> <p class="article-section-sub-title article-section-sub-title-level-2"><h3>5. Statistical analysis</h3></p> <p>The results of this study were principally derived through the utilization of analytical tools, specifically Review Manager software (version 5.4) and Stata software (version 14.0). This diagnostic meta-analysis integrated pooled sensitivity, pooled specificity, along with negative and positive likelihood ratios (NLRs and PLRs), together with corresponding 95% confidence intervals (CIs), to assess the predictive efficacy of MRI-based radiomics concerning preoperative LVSI. The potency of prediction is accentuated when sensitivity and specificity values approximate 1.0, indicating elevated levels of accuracy [<span class="xref"><span id="XREF_B22" class="ref-destination-back"></span><a href="#B22" data-id="XREF_B22" data-toggle="ref-popover" data-trigger="manual" data-placement="right">22</a></span>]. The extent of heterogeneity within the analysis outcomes was gauged through the utilization of the I<sup>2</sup> statistic [<span class="xref"><span id="XREF_B23" class="ref-destination-back"></span><a href="#B23" data-id="XREF_B23" data-toggle="ref-popover" data-trigger="manual" data-placement="right">23</a></span>]. The I<sup>2</sup> metric was categorized into four tiers predicted on percentages: very low heterogeneity (0%–25%), low heterogeneity (25%–50%), medium heterogeneity (50%–75%), and high heterogeneity (>75%). Detection of publication bias was accomplished utilizing Deeks funnel plots [<span class="xref"><span id="XREF_B23" class="ref-destination-back"></span><a href="#B23" data-id="XREF_B23" data-toggle="ref-popover" data-trigger="manual" data-placement="right">23</a></span>]. Furthermore, the deployment of forest plots and Summary Receiver Operating Characteristics (SROC) plots was implemented to enhance the visual comprehensibility of the analysis results.</p> </div> </div> <div> <div><div class="article-section-header"> <div class="__SECTION__" id="__ID_SECTION_3" data-section-name="RESULTS"><div class="dropdown"> <a href="#" class="dropdown-goto dropdown-toggle d-none" id="dropdownGoto_3" title="Go to other sections in this page" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Go to:</a><div class="dropdown-menu dropdown-menu-right d-none" aria-labelledby="dropdownGoto_3"></div> </div></div> <h2>RESULTS</h2> </div></div> <div id="sec1___1"> <p class="article-section-sub-title article-section-sub-title-level-2"><h3>1. Research selection</h3></p> <p>From diverse databases, a cumulative total of 438 studies were initially identified as potential candidates in accordance with the predetermined inclusion and exclusion criteria. Post duplication removal, this number was streamlined to 82. Subsequent evaluation of the titles and abstracts led to the exclusion of 331 studies that failed to meet conformity standards. Thereafter, an in-depth assessment of the complete text was undertaken for the remaining set of 25 articles. Following this comprehensive evaluation, a final selection of 9 studies was deemed appropriate for inclusion within this meta-analysis. The schematic representation of the sequential progression of literature screening is visually depicted in <span class="fig-table-link"><a href="#F1">Fig. 1</a></span>.<div class="image-at-section"></div></p> <p><div class="article-fig image-at-section"><div class="article-figure-table" id="F1"><div class="article-figure-table-card" id="FIGURE-F1" data-type="F" data-afn="1114_JGO_36_X_e26" data-fn="jgo-36-e26-g001_1114JGO"><div class="card border-0 my-3"><div class="row no-gutters"> <div class="col-md-4 mx-auto d-flex justify-content-center flex-wrap align-middle"><div class="card-body"> <a href="#" class="article-figure-table-btn" data-type="F"><img class="align-top" src="/ArticleImage/1114JGO/jgo-36-e26-g001-m.jpg"></a> </div></div> <div class="col-md-8"><div class="card-body"> <p class="card-text"><span class="label"><a href="#" class="article-figure-table-btn" data-type="F">Fig. 1</a> </span><span class="capture-id"><br><span class="capture-id">Flow chart of literature screening.</span></span></p> <ul class="list-inline"> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm article-figure-table-btn" data-type="F">Click for larger image</a></li> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm" href="ViewImagePopup.php?Type=FP&id=F1&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g001_1114JGO" target="_blank">Download as PowerPoint slide</a></li> </ul> </div></div> </div></div></div></div></div></p> </div> <div id="sec2___1"> <p class="article-section-sub-title article-section-sub-title-level-2"><h3>2. Features of included researches</h3></p> <p>The selection for inclusion within this meta-analysis comprises 9 retrospective studies. The cohort sizes across these studies exhibited a range from 56 to 233 patients. Among the nine studies, a solitary study refrained from partitioning patients, while the remainder bifurcated patients into training and validation groups. The criterion for evaluating lymphatic vessel status was consistently anchored in pathological reports as the reference standard. Within the pool, two studies were conducted across multiple centers, while the remaining seven were undertaken at single centers. In conjunction with radiomic features, six studies augmented their analytical framework through the inclusion of patient-derived clinicopathological factors to further elucidate the predictive effect. Among the studies, radiomic features derived from diverse MRI sequences were employed: two studies harnessed T2-weighted imaging (T2WI) sequences [<span class="xref"><span id="XREF_B24" class="ref-destination-back"></span><a href="#B24" data-id="XREF_B24" data-toggle="ref-popover" data-trigger="manual" data-placement="right">24</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B25" class="ref-destination-back"></span><a href="#B25" data-id="XREF_B25" data-toggle="ref-popover" data-trigger="manual" data-placement="right">25</a></span>]; one focused on T1 contrast-enhanced (T1CE) sequences [<span class="xref"><span id="XREF_B26" class="ref-destination-back"></span><a href="#B26" data-id="XREF_B26" data-toggle="ref-popover" data-trigger="manual" data-placement="right">26</a></span>]; two relied upon a combination of T1CE and T2WI sequences [<span class="xref"><span id="XREF_B27" class="ref-destination-back"></span><a href="#B27" data-id="XREF_B27" data-toggle="ref-popover" data-trigger="manual" data-placement="right">27</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B28" class="ref-destination-back"></span><a href="#B28" data-id="XREF_B28" data-toggle="ref-popover" data-trigger="manual" data-placement="right">28</a></span>]; one was grounded in T2WI, T2 with fat suppression (T2FS), diffusion-weighted imaging (DWI), and axial dynamic contrast-enhanced (DCE) sequences [<span class="xref"><span id="XREF_B29" class="ref-destination-back"></span><a href="#B29" data-id="XREF_B29" data-toggle="ref-popover" data-trigger="manual" data-placement="right">29</a></span>]; another was based on a combination of T2WI, T2FS, T1CE, and apparent diffusion coefficient (ADC) [<span class="xref"><span id="XREF_B30" class="ref-destination-back"></span><a href="#B30" data-id="XREF_B30" data-toggle="ref-popover" data-trigger="manual" data-placement="right">30</a></span>]; while one study amalgamated T1-weighted imaging (T1WI), T2FS, DWI, ADC, and contrast-enhanced (CE) sequences [<span class="xref"><span id="XREF_B31" class="ref-destination-back"></span><a href="#B31" data-id="XREF_B31" data-toggle="ref-popover" data-trigger="manual" data-placement="right">31</a></span>], and yet another integrated TIWI, T2FS, and CE sequences [<span class="xref"><span id="XREF_B32" class="ref-destination-back"></span><a href="#B32" data-id="XREF_B32" data-toggle="ref-popover" data-trigger="manual" data-placement="right">32</a></span>]. A comprehensive overview of the foundational attributes of the incorporated studies is provided in <span class="fig-table-link"><a href="#T1">Table 1</a></span>.<div class="image-at-section"></div></p> <p><div class="article-table image-at-section"><div class="article-figure-table" id="T1"><div class="article-figure-table-card" id="TABLE-T1" data-type="T" data-afn="1114_JGO_36_X_e26" data-fn="jgo-36-e26-i001_1114JGO"><div class="card border-0 my-3"><div class="row no-gutters"> <div class="col-md-4 mx-auto d-flex justify-content-center flex-wrap align-middle"><div class="card-body"> <a href="#" class="article-figure-table-btn" data-type="TH"><img class="align-top" src="/ArticleImage/1114JGO/jgo-36-e26-i001-m.jpg"></a> </div></div> <div class="col-md-8"><div class="card-body"> <p class="card-text"><span class="label"><a href="#" class="article-figure-table-btn" data-type="T">Table 1</a> </span><span class="capture-id"><br><span class="capture-id">The synopsis characteristics of included investigators</span></span></p> <ul class="list-inline"> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm article-figure-table-btn" data-type="T">Click for larger image</a></li> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm article-figure-table-btn" data-type="TH">Click for full table</a></li> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm" href="ViewImagePopup.php?Type=TX&id=T1&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-i001_1114JGO" target="_blank">Download as Excel file</a></li> </ul> </div></div> </div></div></div></div></div></p> </div> <div id="sec3___1"> <p class="article-section-sub-title article-section-sub-title-level-2"><h3>3. Biased risk assessment</h3></p> <p>As illustrated in the <span class="fig-table-link"><a href="#T2">Table 2</a></span> and <span class="fig-table-link"><a href="#F2">Fig. 2</a></span>, we employed the RQS and QUADAS-2 assessment scale tool to gauge the risk within the enrolled studies. The average score of the RQS assessment is 15, accounting for 42% of the perfect score, which indicates that the quality of the included articles is poor to moderate. The primary reasons affecting the quality of research include lack of prospective studies and multi-center studies, failure to conduct cost-benefit analysis, and failure to disclose the code available for research. On the contrary, the researches have higher quality in image protocol quality, image segmentation methods, feature dimensionality reduction, internal verification and gold standard reference. The outcomes revealed that a majority of the studies were ascribed a classification of low-to-medium risk. With regard to patient selection, five studies were appraised as harboring low risk, whereas four studies garnered an indeterminate rating due to their omission of explicit indication regarding consecutive patient inclusion. Analogously, this indeterminacy extended to five studies in relation to applicability. Concerning the index test, four studies merited a classification of low risk, substantiated by a low level of concern in terms of applicability, primarily owing to their delineation of radiologist readers’ lack of awareness regarding the reference standard. However, five studies bore the classification of undetermined risk and exhibited an unclear level of concern in applicability, with the exception of one study wherein a sole radiologist reader’s involvement garnered high risk and concomitantly heightened concern in terms of applicability. In the context of reference standard, most studies were designated as carrying an undetermined risk, stemming from the absence of documentation pertaining to pathologists’ blinding to the MRI findings. With regard to applicability, the reliance on pathological outcomes as the gold standard for the majority of tumors culminated in a classification of low concern. Finally, concerning the facet of flow and timing, nearly all studies, bar one, effectively incorporated imaging time, thus rendering them susceptible to low-risk categorization.<div class="image-at-section"></div><div class="image-at-section"></div></p> <p><div class="article-table image-at-section"><div class="article-figure-table" id="T2"><div class="article-figure-table-card" id="TABLE-T2" data-type="T" data-afn="1114_JGO_36_X_e26" data-fn="jgo-36-e26-i002_1114JGO"><div class="card border-0 my-3"><div class="row no-gutters"> <div class="col-md-4 mx-auto d-flex justify-content-center flex-wrap align-middle"><div class="card-body"> <a href="#" class="article-figure-table-btn" data-type="TH"><img class="align-top" src="/ArticleImage/1114JGO/jgo-36-e26-i002-m.jpg"></a> </div></div> <div class="col-md-8"><div class="card-body"> <p class="card-text"><span class="label"><a href="#" class="article-figure-table-btn" data-type="T">Table 2</a> </span><span class="capture-id"><br><span class="capture-id">Radiomic quality scores for all included studies</span></span></p> <ul class="list-inline"> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm article-figure-table-btn" data-type="T">Click for larger image</a></li> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm article-figure-table-btn" data-type="TH">Click for full table</a></li> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm" href="ViewImagePopup.php?Type=TX&id=T2&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-i002_1114JGO" target="_blank">Download as Excel file</a></li> </ul> </div></div> </div></div></div></div></div></p> <p><div class="article-fig image-at-section"><div class="article-figure-table" id="F2"><div class="article-figure-table-card" id="FIGURE-F2" data-type="F" data-afn="1114_JGO_36_X_e26" data-fn="jgo-36-e26-g002_1114JGO"><div class="card border-0 my-3"><div class="row no-gutters"> <div class="col-md-4 mx-auto d-flex justify-content-center flex-wrap align-middle"><div class="card-body"> <a href="#" class="article-figure-table-btn" data-type="F"><img class="align-top" src="/ArticleImage/1114JGO/jgo-36-e26-g002-m.jpg"></a> </div></div> <div class="col-md-8"><div class="card-body"> <p class="card-text"><span class="label"><a href="#" class="article-figure-table-btn" data-type="F">Fig. 2</a> </span><span class="capture-id"><br><span class="capture-id">Results of risk assessment of bias based on QUADAS-2 scale. (A) Assessment of bias based on each study. (B) Summary of the risk of bias across all studies.</span></span></p> <ul class="list-inline"> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm article-figure-table-btn" data-type="F">Click for larger image</a></li> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm" href="ViewImagePopup.php?Type=FP&id=F2&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g002_1114JGO" target="_blank">Download as PowerPoint slide</a></li> </ul> </div></div> </div></div></div></div></div></p> </div> <div id="sec4___1"> <p class="article-section-sub-title article-section-sub-title-level-2"><h3>4. Diagnostic accuracy of MRI-based radiomic</h3></p> <p>From a cumulative pool of 1,406 patients extracted from the amalgamated cohort of 9 studies considered within this meta-analysis, the overall performance metrics of MRI-based radiomics in predicting LVSI in CC patients yielded a sensitivity of 83% (95% CI: 77-87%) and a specificity of 74% (95% CI: 69-79%), as portrayed in <span class="fig-table-link"><a href="#F3">Fig. 3</a></span>. The SROC produced an area under the curve (AUC) value of 0.86 (95% CI: 82-88%) according to <span class="fig-table-link"><a href="#F4">Fig. 4</a></span>. The diagnostic odds ratio (DOR), PLR, and NLR manifested as 14 (95% CI=9–22), 3.22 (95% CI=2.58–4.03), and 0.23 (95% CI=0.17–0.32), respectively. Both sensitivity (I<sup>2</sup>=8.36%) and specificity (I<sup>2</sup>=0) exhibited minimal heterogeneity, as quantified by the I<sup>2</sup> statistics. Notably, <span class="fig-table-link"><a href="#F5">Fig. 5</a></span> visually communicates the outcomes of Deeks funnel plot analysis, which indicate an absence of significant publication bias (p=0.240).<div class="image-at-section"></div><div class="image-at-section"></div><div class="image-at-section"></div></p> <p><div class="article-fig image-at-section"><div class="article-figure-table" id="F3"><div class="article-figure-table-card" id="FIGURE-F3" data-type="F" data-afn="1114_JGO_36_X_e26" data-fn="jgo-36-e26-g003_1114JGO"><div class="card border-0 my-3"><div class="row no-gutters"> <div class="col-md-4 mx-auto d-flex justify-content-center flex-wrap align-middle"><div class="card-body"> <a href="#" class="article-figure-table-btn" data-type="F"><img class="align-top" src="/ArticleImage/1114JGO/jgo-36-e26-g003-m.jpg"></a> </div></div> <div class="col-md-8"><div class="card-body"> <p class="card-text"><span class="label"><a href="#" class="article-figure-table-btn" data-type="F">Fig. 3</a> </span><span class="capture-id"><br><span class="capture-id">Forest plot of sensitivity and specificity for magnetic resonance imaging-based radiomics in predicting lymph-vascular space infiltration of cervical cancer.</span><br><span class="capture-id">CI, confidence interval.</span></span></p> <ul class="list-inline"> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm article-figure-table-btn" data-type="F">Click for larger image</a></li> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm" href="ViewImagePopup.php?Type=FP&id=F3&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g003_1114JGO" target="_blank">Download as PowerPoint slide</a></li> </ul> </div></div> </div></div></div></div></div></p> <p><div class="article-fig image-at-section"><div class="article-figure-table" id="F4"><div class="article-figure-table-card" id="FIGURE-F4" data-type="F" data-afn="1114_JGO_36_X_e26" data-fn="jgo-36-e26-g004_1114JGO"><div class="card border-0 my-3"><div class="row no-gutters"> <div class="col-md-4 mx-auto d-flex justify-content-center flex-wrap align-middle"><div class="card-body"> <a href="#" class="article-figure-table-btn" data-type="F"><img class="align-top" src="/ArticleImage/1114JGO/jgo-36-e26-g004-m.jpg"></a> </div></div> <div class="col-md-8"><div class="card-body"> <p class="card-text"><span class="label"><a href="#" class="article-figure-table-btn" data-type="F">Fig. 4</a> </span><span class="capture-id"><br><span class="capture-id">SROC plot of diagnostic performance in predicting lymph-vascular space infiltration cervical cancer of the included magnetic resonance imaging-based radiomic models.</span><br><span class="capture-id">AUC, area under the curve; SENS, sensitivity; SPEC, specificity; SROC, summary receiver operating characteristic.</span></span></p> <ul class="list-inline"> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm article-figure-table-btn" data-type="F">Click for larger image</a></li> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm" href="ViewImagePopup.php?Type=FP&id=F4&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g004_1114JGO" target="_blank">Download as PowerPoint slide</a></li> </ul> </div></div> </div></div></div></div></div></p> <p><div class="article-fig image-at-section"><div class="article-figure-table" id="F5"><div class="article-figure-table-card" id="FIGURE-F5" data-type="F" data-afn="1114_JGO_36_X_e26" data-fn="jgo-36-e26-g005_1114JGO"><div class="card border-0 my-3"><div class="row no-gutters"> <div class="col-md-4 mx-auto d-flex justify-content-center flex-wrap align-middle"><div class="card-body"> <a href="#" class="article-figure-table-btn" data-type="F"><img class="align-top" src="/ArticleImage/1114JGO/jgo-36-e26-g005-m.jpg"></a> </div></div> <div class="col-md-8"><div class="card-body"> <p class="card-text"><span class="label"><a href="#" class="article-figure-table-btn" data-type="F">Fig. 5</a> </span><span class="capture-id"><br><span class="capture-id">Deeks funnel plot to examine publication bias.</span></span></p> <ul class="list-inline"> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm article-figure-table-btn" data-type="F">Click for larger image</a></li> <li class="list-inline-item"><a role="button" class="btn btn-outline-article btn-sm" href="ViewImagePopup.php?Type=FP&id=F5&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g005_1114JGO" target="_blank">Download as PowerPoint slide</a></li> </ul> </div></div> </div></div></div></div></div></p> </div> </div> <div> <div><div class="article-section-header"> <div class="__SECTION__" id="__ID_SECTION_4" data-section-name="DISCUSSION"><div class="dropdown"> <a href="#" class="dropdown-goto dropdown-toggle d-none" id="dropdownGoto_4" title="Go to other sections in this page" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Go to:</a><div class="dropdown-menu dropdown-menu-right d-none" aria-labelledby="dropdownGoto_4"></div> </div></div> <h2>DISCUSSION</h2> </div></div> <p>A preceding investigation has postulated the significance of LVSI as a pivotal determinant influencing the outcomes and prognosis of CC [<span class="xref"><span id="XREF_B33" class="ref-destination-back"></span><a href="#B33" data-id="XREF_B33" data-toggle="ref-popover" data-trigger="manual" data-placement="right">33</a></span>]. And the prospective trial conducted by the Gynecologic Oncology Group has corroborated the independent prognostic influence of factors such as tumor diameter, presence of LVSI, and interstitial infiltration on the 3-year disease-free survival among CC patients [<span class="xref"><span id="XREF_B34" class="ref-destination-back"></span><a href="#B34" data-id="XREF_B34" data-toggle="ref-popover" data-trigger="manual" data-placement="right">34</a></span>]. The diagnostic discernment of LVSI assumes a crucial role in shaping the therapeutic strategies tailored for individuals afflicted with CC [<span class="xref"><span id="XREF_B7" class="ref-destination-back"></span><a href="#B7" data-id="XREF_B7" data-toggle="ref-popover" data-trigger="manual" data-placement="right">7</a></span>]. However, the contemporary diagnosis of LVSI primarily hinges on surgical pathology. Therefore, the exploration of non-invasive preoperative diagnostic modalities designed to ascertain the LVSI status in advance is imperative. Within our meta-analysis, we embarked on a methodical evaluation of the diagnostic capability attributed to MRI-based radiomic models in predicting LVSI within the context of CC, drawing insights from the scrutiny of 9 included studies. Although the realm of radiomics has progressively captured the attention of researchers in recent years, this study represents the pioneer endeavor to orchestrate a meta-analysis focusing on this domain. The findings unveiled sensitivity and specificity metrics of 83% (95% CI=77%–87%) and 74% (95% CI=69%–79%), respectively, in predicting preoperative LVSI among CC patients via MRI-based radiomics. Meanwhile, the AUC of SROC equated to 0.86 (95% CI=82%–88%). Concomitantly, the DOR, PLR, and NLR were appraised at 14 (95% CI=9–22), 3.22 (95% CI=2.58–4.03), and 0.23 (95% CI=0.17–0.32), respectively. Collectively, these outcomes signify the heightened predictive potential of MRI-based radiomics in the context of preoperative LVSI prediction among CC patients.</p> <p>Currently, a diverse array of imaging modalities, including CT, MR, and PET-CT, are employed for the diagnostic assessment and longitudinal monitoring of CC. Notably, MRI has garnered widespread adoption as an indispensable examination technology. The various imaging findings contribute to physicians’ capacity to delineate patient conditions and establish personalized treatment plans consonant with the disease’s stage. Nonetheless, the diverse interpretive capabilities among healthcare professionals and the inherent limitations in the human eye’s perceptual scope engender marked variability in MRI’s precise applicability to CC across different studies. Consequently, apprehensions persist that the full spectrum of information within imaging data remains latent. In recent years, a surging body of researchers has delved into novel computer algorithms to plumb the concealed depths of information encrypted within imaging data [<span class="xref"><span id="XREF_B35" class="ref-destination-back"></span><a href="#B35" data-id="XREF_B35" data-toggle="ref-popover" data-trigger="manual" data-placement="right">35</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B36" class="ref-destination-back"></span><a href="#B36" data-id="XREF_B36" data-toggle="ref-popover" data-trigger="manual" data-placement="right">36</a></span>]. Simultaneously, the ascent of artificial intelligence has occasioned the emergence of radiomic technology within the medical sphere. Through the deployment of artificial intelligence algorithms, radiomics engenders the extraction of high-dimensional features from a variety of medical images, and these features are subsequently subjected to meticulous analysis and model construction [<span class="xref"><span id="XREF_B37" class="ref-destination-back"></span><a href="#B37" data-id="XREF_B37" data-toggle="ref-popover" data-trigger="manual" data-placement="right">37</a></span>]. An extant meta-analysis recently demonstrated that MRI-based radiomics boasts a favorable predictive utility for lymph node metastasis in CC, with an overall sensitivity of 80% and a specificity of 76% [<span class="xref"><span id="XREF_B38" class="ref-destination-back"></span><a href="#B38" data-id="XREF_B38" data-toggle="ref-popover" data-trigger="manual" data-placement="right">38</a></span>]. Notably, within this context, this study stands as a pioneering endeavor, embarking on a systematic review and meta-analysis of prevailing investigations concerning radiomics’ potential for predicting preoperative LVSI within CC. Accurate preoperative detection of LVSI could potentially mitigate the necessity of surgery, thereby sparing certain patients the associated trauma and financial strain.</p> <p>The potential of radiomics to improve clinical-decision support systems is undeniable and the field is rapidly advancing [<span class="xref"><span id="XREF_B12" class="ref-destination-back"></span><a href="#B12" data-id="XREF_B12" data-toggle="ref-popover" data-trigger="manual" data-placement="right">12</a></span>]. The principal challenge is to optimize the integration of data in a quantitative manner and to provide stable and accurate clinical data [<span class="xref"><span id="XREF_B39" class="ref-destination-back"></span><a href="#B39" data-id="XREF_B39" data-toggle="ref-popover" data-trigger="manual" data-placement="right">39</a></span>]. Radiomics’ potential for predicting LVSI has been exhibited in the context of breast cancer [<span class="xref"><span id="XREF_B20" class="ref-destination-back"></span><a href="#B20" data-id="XREF_B20" data-toggle="ref-popover" data-trigger="manual" data-placement="right">20</a></span>] and endometrial cancer [<span class="xref"><span id="XREF_B40" class="ref-destination-back"></span><a href="#B40" data-id="XREF_B40" data-toggle="ref-popover" data-trigger="manual" data-placement="right">40</a></span>]. Our study additionally underscored the notable potential of radiomics in predicting LVSI within CC. Among the studies included in this analysis, the prevalence of LVSI ranged from 27.2% to 64.8%. Specifically, five studies reported LVSI prevalence exceeding 40% [<span class="xref"><span id="XREF_B24" class="ref-destination-back"></span><a href="#B24" data-id="XREF_B24" data-toggle="ref-popover" data-trigger="manual" data-placement="right">24</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B26" class="ref-destination-back"></span><a href="#B26" data-id="XREF_B26" data-toggle="ref-popover" data-trigger="manual" data-placement="right">26</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B27" class="ref-destination-back"></span><a href="#B27" data-id="XREF_B27" data-toggle="ref-popover" data-trigger="manual" data-placement="right">27</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B29" class="ref-destination-back"></span><a href="#B29" data-id="XREF_B29" data-toggle="ref-popover" data-trigger="manual" data-placement="right">29</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B31" class="ref-destination-back"></span><a href="#B31" data-id="XREF_B31" data-toggle="ref-popover" data-trigger="manual" data-placement="right">31</a></span>]. Notably, four of these five studies were conducted within sizable tertiary healthcare institutions, whereby the tendency of patients with intricate ailments to seek care in higher-tier medical facilities likely contributed to the heightened LVSI prevalence observed. Despite radiomics’ endorsed proficiency in LVSI prediction, quality assessments of the included studies have divulged variances in their quality. The RQS rating ranges from 6 to 18, with the full score is 36 points. Although the widespread application of RQS scores in radiomic evaluations, standardizing quality assessments remains a challenge. Furthermore, the results of the QUADAS-2 evaluation have highlighted pertinent concerns for future studies, including considerations about transparent reporting of patient inclusion criteria and the enforcement of double-blind protocols. Among the studies incorporated in this analysis, a mere two have undergone external validation. Consistent with prevailing meta-analyses, the analyzed literature lacks cost variance analyses [<span class="xref"><span id="XREF_B38" class="ref-destination-back"></span><a href="#B38" data-id="XREF_B38" data-toggle="ref-popover" data-trigger="manual" data-placement="right">38</a></span><span class="xref"><span class="gen">, </span><span id="XREF_B41" class="ref-destination-back"></span><a href="#B41" data-id="XREF_B41" data-toggle="ref-popover" data-trigger="manual" data-placement="right">41</a></span>]. Augmenting external validation and cost-effectiveness evaluations could further delineate the clinical significance and pragmatic viability of the proposed models. Significantly, all studies encompassed within this analysis adopt a retrospective design, consequently implicating a risk of selective reporting that favors positive outcomes. In light of this, the imperative to undertake additional prospective investigations for validating the veracity of these novel models is underscored [<span class="xref"><span id="XREF_B42" class="ref-destination-back"></span><a href="#B42" data-id="XREF_B42" data-toggle="ref-popover" data-trigger="manual" data-placement="right">42</a></span>].</p> <p>While our study imparts noteworthy insights into the potential utility of MRI-based radiomics for preoperative LVSI diagnosis in CC, it is imperative to acknowledge the presence of certain limitations within this study. Foremost, the scope of our inclusivity is delimited by a relatively modest array of studies, constituting a total of nine studies enrolling 1,406 patients. However, it should be recognized that the integration of radiomic methodologies into the CC diagnostic framework remains incipient, thus warranting comprehensive exploration across diversified approaches. Secondly, all included studies adopt a retrospective design, a factor that introduces the potential for selection bias. It is pertinent to note that merely two studies underwent external validation through a multicenter approach. Consequently, forthcoming endeavors should prioritize the conduct of prospective multicenter studies, thereby amplifying the validation quotient of radiomics’ predictive efficacy in the CC context. Thirdly, incongruities exist in terms of image sequences employed across the included studies, potentially engendering repercussions for result evaluation. Fourthly, the paucity of comprehensive patient characteristics within certain studies has the potential to constrain the applicability of results within clinical scenarios. Fifthly, the non-explication of the temporal interval between imaging and surgery within two studies could conceivably exert an impact on disease staging accuracy. In parallel, the type of contrast media, imaging apparatus, and analytical software could wield influence over radiomic analysis results. Consequently, an earnest anticipation is directed towards the emergence of future research endeavors that embrace standardized amalgamation of artificial intelligence and medical paradigms, thereby fostering enhancements in the diagnostic and therapeutic dimensions for cancer patients.</p> </div> <div> <div><div class="article-section-header"> <div class="__SECTION__" id="__ID_SECTION_5" data-section-name="CONCLUSION"><div class="dropdown"> <a href="#" class="dropdown-goto dropdown-toggle d-none" id="dropdownGoto_5" title="Go to other sections in this page" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Go to:</a><div class="dropdown-menu dropdown-menu-right d-none" aria-labelledby="dropdownGoto_5"></div> </div></div> <h2>CONCLUSION</h2> </div></div> <p>In conclusion, our study underscores that MRI-based radiomics holds substantial diagnostic utility in predicting preoperative LVSI among patients afflicted with CC. The consolidated sensitivity and specificity values of 83% and 74%, respectively, signify robust predictive potential. Predicting LVSI status prior to surgical intervention bears the potential to mitigate the need for invasive procedures, while simultaneously facilitating the early formulation of personalized therapeutic regimens. However, the validation of radiomic-based models necessitates further exploration through prospective, multi-center, and large-scale investigations before their clinical integration can be substantiated.</p> </div> <div id="supplementary-material"><p><a id="#supplementary-material-sec" name="#supplementary-material-sec"></a> <div><div class="article-section-header"> <div class="__SECTION__" id="__ID_SECTION_6" data-section-name="SUPPLEMENTARY MATERIAL"><div class="dropdown"> <a href="#" class="dropdown-goto dropdown-toggle d-none" id="dropdownGoto_6" title="Go to other sections in this page" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Go to:</a><div class="dropdown-menu dropdown-menu-right d-none" aria-labelledby="dropdownGoto_6"></div> </div></div> <h2>SUPPLEMENTARY MATERIAL</h2> </div></div> <p class="supplementary-material-item"><span class="capture-id"> <h3>Data S1</h3> <p>Search terms.</p> </span><a href="DownloadSupplMaterial.php?id=10.3802/jgo.2025.36.e26&fn=jgo-36-e26-s001.doc" id="S1">Click here to view.</a><sup class="supplementary-material-media-attribute">(25K, doc)</sup> </p></p></div> </div> <div id="article-level-0-figs-and-tables" class="bm bm-figure-tables"> </div> <div id="article-level-0-back" class="bm"> <div class="article-section-header"> <div class="__SECTION__" id="__ID_SECTION_7" data-section-name="Notes"><div class="dropdown"> <a href="#" class="dropdown-goto dropdown-toggle d-none" id="dropdownGoto_7" title="Go to other sections in this page" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Go to:</a><div class="dropdown-menu dropdown-menu-right d-none" aria-labelledby="dropdownGoto_7"></div> </div></div> <div class="article-section-title">Notes</div> </div> <div id=""><p><span class="fn-label">Conflict of Interest:</span>No potential conflict of interest relevant to this article was reported.</p></div> <div id=""><p><span class="fn-label">Data Availability:</span>Data are available upon reasonable request.</p></div> <div id=""><p><span class="fn-label">Author Contributions:</span> <div> <ul class="listitem" style="list-style-type: none;"> <li class="listitem"><p><b>Conceptualization:</b> Z.M., F.J.</p></li> <li class="listitem"><p><b>Data curation:</b> G.X., Y.X., G.Z., W.S.</p></li> <li class="listitem"><p><b>Formal analysis:</b> L.Z., G.X., F.J.</p></li> <li class="listitem"><p><b>Methodology:</b> F.J.</p></li> <li class="listitem"><p><b>Software:</b> Y.X., G.Z., W.S.</p></li> <li class="listitem"><p><b>Validation:</b> G.Z., W.S.</p></li> <li class="listitem"><p><b>Visualization:</b> Y.X.</p></li> <li class="listitem"><p><b>Writing - original draft:</b> Z.M.</p></li> <li class="listitem"><p><b>Writing - review & editing:</b> L.Z., F.J.</p></li> </ul> </div></p></div> <div class="references"> <div class="__SECTION__" id="__ID_SECTION_8" data-section-name="References"><div class="dropdown"> <a href="#" class="dropdown-goto dropdown-toggle d-none" id="dropdownGoto_8" title="Go to other sections in this page" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Go to:</a><div class="dropdown-menu dropdown-menu-right d-none" aria-labelledby="dropdownGoto_8"></div> </div></div> <h3 class="title">References</h3> </div> <div class="bm"> <tr> <td></td> <td></td> </tr> <ol> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B1" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B1_DATA" class="ref-data" valign="top">Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71:209–249.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/33538338" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.3322%2Fcaac.21660" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B2" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B2_DATA" class="ref-data" valign="top">Landoni F, Maneo A, Colombo A, Placa F, Milani R, Perego P, et al. Randomised study of radical surgery versus radiotherapy for stage Ib-IIa cervical cancer. Lancet 1997;350:535–540.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/9284774" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2FS0140-6736%2897%2902250-2" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B3" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B3_DATA" class="ref-data" valign="top">Landoni F, Colombo A, Milani R, Placa F, Zanagnolo V, Mangioni C. Randomized study between radical surgery and radiotherapy for the treatment of stage IB-IIA cervical cancer: 20-year update. J Gynecol Oncol 2017;28:e34<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/28382797" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.3802%2Fjgo.2017.28.e34" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B4" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B4_DATA" class="ref-data" valign="top">Abu-Rustum NR, Yashar CM, Bean S, Bradley K, Campos SM, Chon HS, et al. NCCN guidelines insights: cervical cancer, version 1.2020. J Natl Compr Canc Netw 2020;18:660–666.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/32502976" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.6004%2Fjnccn.2020.0027" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B5" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B5_DATA" class="ref-data" valign="top">Padera TP, Kadambi A, di Tomaso E, Carreira CM, Brown EB, Boucher Y, et al. Lymphatic metastasis in the absence of functional intratumor lymphatics. Science 2002;296:1883–1886.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/11976409" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1126%2Fscience.1071420" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B6" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B6_DATA" class="ref-data" valign="top">Pol FJ, Zusterzeel PL, van Ham MA, Kuijpers DA, Bulten J, Massuger LF. Satellite lymphovascular space invasion: an independent risk factor in early stage cervical cancer. Gynecol Oncol 2015;138:579–584.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/26126782" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.ygyno.2015.06.035" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B7" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B7_DATA" class="ref-data" valign="top">Bidus MA, Caffrey AS, You WB, Amezcua CA, Chernofsky MR, Barner R, et al. Cervical biopsy and excision procedure specimens lack sufficient predictive value for lymph-vascular space invasion seen at hysterectomy for cervical cancer. Am J Obstet Gynecol 2008;199:151.e1–151.e4.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/18674657" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.ajog.2008.02.017" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B8" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B8_DATA" class="ref-data" valign="top">Alessandrino F. Editorial for: “Novel T2 Mapping for Evaluating Cervical Cancer Features by Providing Quantitative T2 Maps and Synthetic Morphologic Images: A Preliminary Study”. J Magn Reson Imaging 2020;52:1870–1871.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/32830384" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1002%2Fjmri.27296" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B9" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B9_DATA" class="ref-data" valign="top">Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563–577.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/26579733" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1148%2Fradiol.2015151169" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B10" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B10_DATA" class="ref-data" valign="top">Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48:441–446.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/22257792" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.ejca.2011.11.036" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B11" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B11_DATA" class="ref-data" valign="top">Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/24892406" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1038%2Fncomms5006" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B12" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B12_DATA" class="ref-data" valign="top">Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2016;2:1636–1642.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/27541161" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1001%2Fjamaoncol.2016.2631" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B13" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B13_DATA" class="ref-data" valign="top">Lambin P, Leijenaar RT, Deist TM, Peerlings J, de Jong EE, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017;14:749–762.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/28975929" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1038%2Fnrclinonc.2017.141" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B14" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B14_DATA" class="ref-data" valign="top">Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 2009;62:1609–1618.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/19859947" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1002%2Fmrm.22147" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B15" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B15_DATA" class="ref-data" valign="top">Wang H, Xu X, Zhang X, Liu Y, Ouyang L, Du P, et al. Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study. Eur Radiol 2020;30:4816–4827.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/32318846" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1007%2Fs00330-020-06796-8" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B16" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B16_DATA" class="ref-data" valign="top">Deng X, Liu M, Sun J, Li M, Liu D, Li L, et al. Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer. Eur J Radiol 2021;134:109429<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/33290975" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.ejrad.2020.109429" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B17" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B17_DATA" class="ref-data" valign="top">Wu Q, Wang S, Zhang S, Wang M, Ding Y, Fang J, et al. Development of a deep learning model to identify lymph node metastasis on magnetic resonance imaging in patients with cervical cancer. JAMA Netw Open 2020;3:e2011625<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/32706384" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1001%2Fjamanetworkopen.2020.11625" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B18" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B18_DATA" class="ref-data" valign="top">Liu C, Ding J, Spuhler K, Gao Y, Serrano Sosa M, Moriarty M, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J Magn Reson Imaging 2019;49:131–140.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/30171822" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1002%2Fjmri.26224" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B19" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B19_DATA" class="ref-data" valign="top">Zhou X, Yi Y, Liu Z, Zhou Z, Lai B, Sun K, et al. Radiomics-based preoperative prediction of lymph node status following neoadjuvant therapy in locally advanced rectal cancer. Front Oncol 2020;10:604.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/32477930" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.3389%2Ffonc.2020.00604" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B20" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B20_DATA" class="ref-data" valign="top">Liu Z, Feng B, Li C, Chen Y, Chen Q, Li X, et al. Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics. J Magn Reson Imaging 2019;50:847–857.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/30773770" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1002%2Fjmri.26688" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B21" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B21_DATA" class="ref-data" valign="top">Yang B, Mallett S, Takwoingi Y, Davenport CF, Hyde CJ, Whiting PF, et al. QUADAS-C: a tool for assessing risk of bias in comparative diagnostic accuracy studies. Ann Intern Med 2021;174:1592–1599.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/34698503" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.7326%2FM21-2234" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B22" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B22_DATA" class="ref-data" valign="top">Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 1959;22:719–748.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/13655060" target="_new"><span>PubMed</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B23" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B23_DATA" class="ref-data" valign="top">Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 2005;58:882–893.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/16085191" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.jclinepi.2005.01.016" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B24" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B24_DATA" class="ref-data" valign="top">Du W, Wang Y, Li D, Xia X, Tan Q, Xiong X, et al. Preoperative prediction of lymphovascular space invasion in cervical cancer with radiomics -based nomogram. Front Oncol 2021;11:637794<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/34322375" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.3389%2Ffonc.2021.637794" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B25" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B25_DATA" class="ref-data" valign="top">Shi J, Cui L, Wang H, Dong Y, Yu T, Yang H, et al. MRI-based intratumoral and peritumoral radiomics on prediction of lymph-vascular space invasion in cervical cancer: a multi-center study. Biomed Signal Process Control 2022;72<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.bspc.2021.103373" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B26" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B26_DATA" class="ref-data" valign="top">Li Z, Li H, Wang S, Dong D, Yin F, Chen A, et al. MR-based radiomics nomogram of cervical cancer in prediction of the lymph-vascular space invasion preoperatively. J Magn Reson Imaging 2019;49:1420–1426.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/30362652" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1002%2Fjmri.26531" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B27" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B27_DATA" class="ref-data" valign="top">Hua WQ, Xiao TH, Jiang XR, Liu Z, Wang M, Zheng H, et al. Lymph-vascular space invasion prediction in cervical cancer: Exploring radiomics and deep learning multilevel features of tumor and peritumor tissue on multiparametric MRI. Biomed Signal Process Control 2020;58:101869<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.bspc.2020.101869" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B28" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B28_DATA" class="ref-data" valign="top">Cui L, Yu T, Kan Y, Dong Y, Luo Y, Jiang X. Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer. Diagn Interv Radiol 2022;28:312–321.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/35731710" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.5152%2Fdir.2022.20657" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B29" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B29_DATA" class="ref-data" valign="top">Wu Q, Shi D, Dou S, Shi L, Liu M, Dong L, et al. Radiomics analysis of multiparametric MRI evaluates the pathological features of cervical squamous cell carcinoma. J Magn Reson Imaging 2019;49:1141–1148.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/30230114" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1002%2Fjmri.26301" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B30" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B30_DATA" class="ref-data" valign="top">Huang G, Cui Y, Wang P, Ren J, Wang L, Ma Y, et al. Multi-parametric magnetic resonance imaging-based radiomics analysis of cervical cancer for preoperative prediction of lymphovascular space invasion. Front Oncol 2022;11:663370<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/35096556" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.3389%2Ffonc.2021.663370" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B31" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B31_DATA" class="ref-data" valign="top">Xiao M, Li Y, Ma F, Zhang G, Qiang J. Multiparametric MRI radiomics nomogram for predicting lymph-vascular space invasion in early-stage cervical cancer. Br J Radiol 2022;95:20211076<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/35312379" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1259%2Fbjr.20211076" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B32" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B32_DATA" class="ref-data" valign="top">Wu Y, Wang S, Chen Y, Liao Y, Yin X, Li T, et al. A multicenter study on preoperative assessment of lymphovascular space invasion in early-stage cervical cancer based on multimodal MR radiomics. J Magn Reson Imaging 2023;58:1638–1648.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/36929220" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1002%2Fjmri.28676" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B33" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B33_DATA" class="ref-data" valign="top">Margolis B, Cagle-Colon K, Chen L, Tergas AI, Boyd L, Wright JD. Prognostic significance of lymphovascular space invasion for stage IA1 and IA2 cervical cancer. Int J Gynecol Cancer 2020;30:735–743.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/32179697" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1136%2Fijgc-2019-000849" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B34" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B34_DATA" class="ref-data" valign="top">Delgado G, Bundy B, Zaino R, Sevin BU, Creasman WT, Major F. Prospective surgical-pathological study of disease-free interval in patients with stage IB squamous cell carcinoma of the cervix: a Gynecologic Oncology Group study. Gynecol Oncol 1990;38:352–357.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/2227547" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2F0090-8258%2890%2990072-S" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B35" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B35_DATA" class="ref-data" valign="top">Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B. Radiomics and artificial intelligence for biomarker and prediction model development in oncology. Comput Struct Biotechnol J 2019;17:995–1008.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/31388413" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.csbj.2019.07.001" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B36" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B36_DATA" class="ref-data" valign="top">Xu Y, Hosny A, Zeleznik R, Parmar C, Coroller T, Franco I, et al. Deep learning predicts lung cancer treatment response from serial medical imaging. Clin Cancer Res 2019;25:3266–3275.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/31010833" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1158%2F1078-0432.CCR-18-2495" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B37" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B37_DATA" class="ref-data" valign="top">Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, et al. Introduction to radiomics. J Nucl Med 2020;61:488–495.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/32060219" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.2967%2Fjnumed.118.222893" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B38" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B38_DATA" class="ref-data" valign="top">Li L, Zhang J, Zhe X, Tang M, Zhang X, Lei X, et al. A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer. Eur J Radiol 2022;151:110243<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/35366583" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.ejrad.2022.110243" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B39" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B39_DATA" class="ref-data" valign="top">Lambin P, Zindler J, Vanneste BG, De Voorde LV, Eekers D, Compter I, et al. Decision support systems for personalized and participative radiation oncology. Adv Drug Deliv Rev 2017;109:131–153.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/26774327" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.addr.2016.01.006" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B40" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B40_DATA" class="ref-data" valign="top">Luo Y, Mei D, Gong J, Zuo M, Guo X. Multiparametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in endometrial carcinoma. J Magn Reson Imaging 2020;52:1257–1262.<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/32315482" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1002%2Fjmri.27142" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B41" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B41_DATA" class="ref-data" valign="top">Zheng X, He B, Hu Y, Ren M, Chen Z, Zhang Z, et al. Diagnostic accuracy of deep learning and radiomics in lung cancer staging: a systematic review and meta-analysis. Front Public Health 2022;10:938113<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/35923964" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.3389%2Ffpubh.2022.938113" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> <li><ol class="references-and-notes-list"><li class="skip-numbering" value="1"> <div id="B42" class="ref-destination"></div> <div class="ref-item"> <span id="XREF_B42_DATA" class="ref-data" valign="top">Stanzione A, Gambardella M, Cuocolo R, Ponsiglione A, Romeo V, Imbriaco M. Prostate MRI radiomics: a systematic review and radiomic quality score assessment. Eur J Radiol 2020;129:109095<div><ul class="list-group list-group-horizontal"> <li class="list-group-item-ref"><a href="https://pubmed.ncbi.nlm.nih.gov/32531722" target="_new"><span>PubMed</span></a></li> <li class="list-group-item-ref"><a href="https://doi.org/10.1016%2Fj.ejrad.2020.109095" target="_new"><span>CrossRef</span></a></li>  </ul></div></span> </div> </li></ol></li> </ol> </div> </div> </div> </div> <div class="article-portlets col-xxl-3 offset-xxl-1 col-xl-3 col-lg-4 col-xs-12">  <div class="d-lg-none"> <hr></hr> </div> <div id="PortletFormatLink" class="porlet"> <ul class="list-group list-group-flush"> <li class="list-group-item ms-0 ps-0 "> <a class="format-item icon-link CiteDialog" href="#" onclick="" data-article-id=10.3802/jgo.2025.36.e26 data-article-citation=WmhhbyBNLCBMaSBaLCBHdSBYLCBZYW5nIFgsIEdhbyBaLCBXYW5nIFMsIEZ1IEouIFRoZSByb2xlIG9mIHJhZGlvbWljcyBmb3IgcHJlZGljdGluZyBvZiBseW1waC12YXNjdWxhciBzcGFjZSBpbnZhc2lvbiBpbiBjZXJ2aWNhbCBjYW5jZXIgcGF0aWVudHMgYmFzZWQgb24gYXJ0aWZpY2lhbCBpbnRlbGxpZ2VuY2U6IGEgc3lzdGVtYXRpYyByZXZpZXcgYW5kIG1ldGEtYW5hbHlzaXMuIEogR3luZWNvbCBPbmNvbC4gMjAyNCBKdWw7MzY6ZTI2LiA8QSBIUkVGPSJodHRwczovL2RvaS5vcmcvMTAuMzgwMi9qZ28uMjAyNS4zNi5lMjYiIHRhcmdldD1fYmxhbms+aHR0cHM6Ly9kb2kub3JnLzEwLjM4MDIvamdvLjIwMjUuMzYuZTI2PC9BPg==> <div> <i aria-hidden="true" class="bi bi-quote"></i> </div> <span class="align-top">Cite</span> </a> </li> <li class="list-group-item ms-0 ps-0 "> <a class="format-item icon-link " href="search.php?where=aview&id=10.3802/jgo.2025.36.e26&code=1114JGO&vmode=FULL" onclick="" > <div> <i aria-hidden="true" class="bi bi-book"></i> </div> <span class="align-top">Article</span> </a> </li> <li class="list-group-item ms-0 ps-0 "> <a class="format-item icon-link " href="/pdf/10.3802/jgo.2025.36.e26" onclick="trackDownloadLink('PDF', '10.3802/jgo.2025.36.e26');" > <div> <i aria-hidden="true" class="bi bi-file-earmark-pdf"></i> </div> <span class="align-top">PDF</span> </a> </li> <li class="list-group-item ms-0 ps-0 "> <a class="format-item icon-link " href="search.php?where=aview&id=10.3802/jgo.2025.36.e26&code=1114JGO&vmode=SM" onclick="" > <div> <i aria-hidden="true" class="bi bi-paperclip"></i> </div> <span class="align-top">Supplementary Materials</span> </a> </li> </ul> </div>    <div class="my-5 border-top porlet"> <div class="modal modal-center" id="myModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog modal-lg modal-dialog-centered" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="myModalLabel"></h5> <button type="button" class="btn btn-outline-primary" data-bs-dismiss="modal">×</button> </div> <div class="modal-body"> <div id="myModalTotalLinkLabel" class="mb-1"></div> <div id="my-chart" class="my-1"></div> <div align="right"><small>Since 2025/01/01</small></div> </div> </div> </div> </div> <div class="my-3"> <div><h5>Metrics</h5></div> </div> <div> <div class="row"> <div class="col-6 border-right pb-1"> <a href="#" class="PageViewLink"> Page Views<br> <span class="text-primary">153</span> </a> </div> <div class="col-6 pb-1"> <a href="#" class="PDFDownloadLink"> PDF Downloads<br> <span class="text-primary">27</span> </a> </div> </div> </div> </div>  <div class="my-5 border-top porlet"> <div class="my-3"> <div><h5>Share</h5></div> </div> <div> <ul class="list-inline"> <li class="list-inline-item"> <A HREF="https://twitter.com/intent/tweet?url=https%3A%2F%2Fejgo.org%2FDOIx.php%3Fid%3D10.3802%2Fjgo.2025.36.e26&text=The+role+of+radiomics+for+predicting+of+lymph-vascular+space+invasion+in+cervical+cancer+patients+based+on+artificial+intelligence%3A+a+systematic+review+and+meta-analysis" data-bs-toggle="tooltip" data-bs-placement="bottom" data-bs-title="Twitter" target="_blank"><i class="bi bi-twitter fs-3"></i></A></li> </li> <li class="list-inline-item"> <A HREF="https://www.facebook.com/sharer/sharer.php?u=https://ejgo.org/DOIx.php?id=10.3802/jgo.2025.36.e26" data-bs-toggle="tooltip" data-bs-placement="bottom" data-bs-title="Facebook" target="_blank"><i class="bi bi-facebook fs-3"></i></A></li> </li> <li class="list-inline-item"> <A data-bs-trigger="click" id="SharePermalink" role="button" data-bs-placement="bottom" data-toggle="article-popover"><i class="bi bi-link-45deg fs-3"></i></A></li> </li> </ul> </div> </div>   <div class="my-5 border-top porlet"> <div class="my-3"> <div class="d-flex"> <div class="flex-grow-1"><h5>Figures</h5></div> <div><small><a href="search.php?where=aview&id=10.3802/jgo.2025.36.e26&code=1114JGO&vmode=FT"><span class="fw-bolder">Show all...</a></a></small></div> </div> </div> <div class="figs-gray w-75 mx-auto"> <div id="article-portlet-figure-carousel" class="article-portlet-carousel carousel slide" data-bs-ride="carousel"> <div class="carousel-inner"> <div class="carousel-item active"> <a href="ViewImage.php?Type=F&aid=794208&id=F1&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g001_1114JGO" class="article-figure-table-link" data-id="FIGURE-F1" data-type="F"><img class="d-block w-100" src="ArticleImage/1114JGO/jgo-36-e26-g001-m.jpg" alt="slide"></a> <div class="carousel-caption d-none d-md-block"> </div> </div> <div class="carousel-item"> <a href="ViewImage.php?Type=F&aid=794208&id=F2&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g002_1114JGO" class="article-figure-table-link" data-id="FIGURE-F2" data-type="F"><img class="d-block w-100" src="ArticleImage/1114JGO/jgo-36-e26-g002-m.jpg" alt="slide"></a> <div class="carousel-caption d-none d-md-block"> </div> </div> <div class="carousel-item"> <a href="ViewImage.php?Type=F&aid=794208&id=F3&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g003_1114JGO" class="article-figure-table-link" data-id="FIGURE-F3" data-type="F"><img class="d-block w-100" src="ArticleImage/1114JGO/jgo-36-e26-g003-m.jpg" alt="slide"></a> <div class="carousel-caption d-none d-md-block"> </div> </div> <div class="carousel-item"> <a href="ViewImage.php?Type=F&aid=794208&id=F4&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g004_1114JGO" class="article-figure-table-link" data-id="FIGURE-F4" data-type="F"><img class="d-block w-100" src="ArticleImage/1114JGO/jgo-36-e26-g004-m.jpg" alt="slide"></a> <div class="carousel-caption d-none d-md-block"> </div> </div> <div class="carousel-item"> <a href="ViewImage.php?Type=F&aid=794208&id=F5&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-g005_1114JGO" class="article-figure-table-link" data-id="FIGURE-F5" data-type="F"><img class="d-block w-100" src="ArticleImage/1114JGO/jgo-36-e26-g005-m.jpg" alt="slide"></a> <div class="carousel-caption d-none d-md-block"> </div> </div> </div> <button class="carousel-control-prev" type="button" data-bs-target="#article-portlet-figure-carousel" data-bs-slide="prev"> <span class="carousel-control-prev-icon" aria-hidden="true"></span> <span class="visually-hidden">Previous</span> </button> <button class="carousel-control-next" type="button" data-bs-target="#article-portlet-figure-carousel" data-bs-slide="next"> <span class="carousel-control-next-icon" aria-hidden="true"></span> <span class="visually-hidden">Next</span> </button> <div> <p class="text-center"><span class="carousel-current-page">1</span> / 5</p> </div> </div> </div> </div>  <div class="my-5 border-top porlet"> <div class="my-3"> <div class="d-flex"> <div class="flex-grow-1"><h5>Tables</h5></div> <div><small><a href="search.php?where=aview&id=10.3802/jgo.2025.36.e26&code=1114JGO&vmode=FT"><span class="fw-bolder">Show all...</span></a></small></div> </div> </div> <div class="w-75 table-gray mx-auto"> <div id="article-portlet-table-carousel" class="article-portlet-carousel carousel slide" data-bs-ride="carousel"> <div class="carousel-inner"> <div class="carousel-item active"> <a href="ViewImage.php?Type=T&aid=794208&id=T1&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-i001_1114JGO" class="article-figure-table-link" data-id="TABLE-T1" data-type="T"><img class="d-block w-100" src="ArticleImage/1114JGO/jgo-36-e26-i001-m.jpg" alt="slide"></a> </div> <div class="carousel-item"> <a href="ViewImage.php?Type=T&aid=794208&id=T2&afn=1114_JGO_36_X_e26&fn=jgo-36-e26-i002_1114JGO" class="article-figure-table-link" data-id="TABLE-T2" data-type="T"><img class="d-block w-100" src="ArticleImage/1114JGO/jgo-36-e26-i002-m.jpg" alt="slide"></a> </div> </div> <button class="carousel-control-prev" type="button" data-bs-target="#article-portlet-table-carousel" data-bs-slide="prev"> <span class="carousel-control-prev-icon" aria-hidden="true"></span> <span class="visually-hidden">Previous</span> </button> <button class="carousel-control-next" type="button" data-bs-target="#article-portlet-table-carousel" data-bs-slide="next"> <span class="carousel-control-next-icon" aria-hidden="true"></span> <span class="visually-hidden">Next</span> </button> <div> <p class="text-center"><span class="carousel-current-page">1</span> / 2</p> </div> </div> </div> </div>  <div class="my-5 border-top porlet"> <div class="my-3"> <div><h5>ORCID IDs</h5></div> </div> <div> <ul class="list-unstyled"> <li> <a href="https://orcid.org/0000-0001-6337-7516" data-toggle="tooltip" data-placement="right" title="https://orcid.org/0000-0001-6337-7516"><span class="align-middle pr-1 text-primary">Mengli Zhao</span> <img src="image/icon-orcid.jpg" border="0" align="absmiddle"></a> </li> <li> <a href="https://orcid.org/0000-0001-9395-8004" data-toggle="tooltip" data-placement="right" title="https://orcid.org/0000-0001-9395-8004"><span class="align-middle pr-1 text-primary">Zhen Li</span> <img src="image/icon-orcid.jpg" border="0" align="absmiddle"></a> </li> <li> <a href="https://orcid.org/0000-0001-8501-5305" data-toggle="tooltip" data-placement="right" title="https://orcid.org/0000-0001-8501-5305"><span class="align-middle pr-1 text-primary">Xiaowei Gu</span> <img src="image/icon-orcid.jpg" border="0" align="absmiddle"></a> </li> <li> <a href="https://orcid.org/0000-0003-4881-9771" data-toggle="tooltip" data-placement="right" title="https://orcid.org/0000-0003-4881-9771"><span class="align-middle pr-1 text-primary">Xiaojing Yang</span> <img src="image/icon-orcid.jpg" border="0" align="absmiddle"></a> </li> <li> <a href="https://orcid.org/0009-0000-0312-3418" data-toggle="tooltip" data-placement="right" title="https://orcid.org/0009-0000-0312-3418"><span class="align-middle pr-1 text-primary">Zhongrong Gao</span> <img src="image/icon-orcid.jpg" border="0" align="absmiddle"></a> </li> <li> <a href="https://orcid.org/0000-0002-1790-3359" data-toggle="tooltip" data-placement="right" title="https://orcid.org/0000-0002-1790-3359"><span class="align-middle pr-1 text-primary">Shanshan Wang</span> <img src="image/icon-orcid.jpg" border="0" align="absmiddle"></a> </li> <li> <a href="https://orcid.org/0000-0002-3183-453X" data-toggle="tooltip" data-placement="right" title="https://orcid.org/0000-0002-3183-453X"><span class="align-middle pr-1 text-primary">Jie Fu</span> <img src="image/icon-orcid.jpg" border="0" align="absmiddle"></a> </li> </ul> </div> </div>  <hr class="navbar-goto-display d-none">  <nav id="navbar-goto" data-bs-spy="scroll" class="sticky-top mb-5" tabindex="0"> <div class="navbar-goto-display d-none"> <ul id="navbar-goto-item" class="nav nav-pills flex-column"> </ul> </div> </nav> </div> </div> </section> </div> <div> <div id="CrossrefCitationListModal" class="modal fade" tabindex="-1" aria-hidden="true"> <div class="modal-dialog modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title">Cited by Crossref</h5> <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button> </div> <div class="modal-body"> <p>Is Cited by the Following Articles in <img src="image/icon_CrossRef.gif"></p> <p class="modal-body-citationlist"></p> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" data-bs-dismiss="modal">Close</button> </div> </div> </div> </div> </div> <div id="CiteModal" class="modal" tabindex="-1"> <div class="modal-dialog modal-md modal-dialog-centered modal-dialog-scrollable"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title">Cite</h5> <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button> </div> <div class="modal-body"> <div id="CiteModalCopyAlert" class="d-none alert alert-primary alert-dismissible fade show" role="alert"> Citation successfully copied. </div> <p>Copy and paste a formatted citation from below or use one of the hyperlinks at the bottom to download a file for import into a bibliography manager.</p> <p id="CiteModalContent" class="w-100 p-3 border"></p> <div id="CiteModalButtons" class="btn-group dropup py-3"> <button id="Portlet-MenuItem-CITATION-Copy" type="button" class="btn btn-outline-primary me-1" aria-label="Copy" data-dismiss="alert" data-original-title="Copy to clipboard">Copy <i class="bi bi-copy"></i></button> <div class="dropdown"> <button class="btn btn-outline-primary me-1" type="button" id="dropdownDownload" data-bs-toggle="dropdown" aria-expanded="false"> Download <i class="bi bi-download"></i> </button> <ul class="dropdown-menu" aria-labelledby="dropdownDownload"> <li><a class="dropdown-item" href="#" data-cr="cr1" data-ex="ex3">EndNote / ProCite / Reference Manager</a></li> <li><a class="dropdown-item" href="#" data-cr="cr1" data-ex="ex2">RIS Format</a></li> <li><a class="dropdown-item" href="#" data-cr="cr1" data-ex="ex1">Plain Text</a></li> </ul> </div> <div class="dropdown"> <button class="btn btn-outline-primary me-1" type="button" id="dropdownDownloadAbstract" data-bs-toggle="dropdown" aria-expanded="false"> Download with Abstract <i class="bi bi-download"></i> </button> <ul class="dropdown-menu" aria-labelledby="dropdownDownloadAbstract"> <li><a class="dropdown-item" href="#" data-cr="cr2" data-ex="ex3">EndNote / ProCite / Reference Manager</a></li> <li><a class="dropdown-item" href="#" data-cr="cr2" data-ex="ex2">RIS Format</a></li> <li><a class="dropdown-item" href="#" data-cr="cr2" data-ex="ex1">Plain Text</a></li> </ul> </div> </div> </div> </div> </div> </div> <div id="FigureTableModal" class="modal fade" tabindex="-1"> <div class="modal-dialog modal-fullscreen modal-dialog-scrollable mx-auto align-middle"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title"></h5> <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button> </div> <div class="modal-body"> </div> </div> </div> </div> <div id="SharePermalinkModal" class="d-none z-n1"> <div data-name="popover-content" class="z-n1"> <div class="d-flex mb-2"> <div class="me-auto">PERMALINK</div> <div></div> </div> <form class="d-flex" role="search"> <textarea id="SharePermalinkText" readonly class="copyCliboardText form-control" disabled rows="4"></textarea> <button id="SharePermalinkCopyClipboardBtn" class="copyclipboard btn btn-primary border" type="btn"><i class="bi bi-clipboard"></i>Copy</button> </form> </div> </div> <div class="toast-container position-fixed bottom-0 end-0 p-3"> <div id="copyCliboardCompleteToast" class="toast align-items-center text-bg-primary border-0" role="alert" aria-live="assertive" aria-atomic="true"> <div class="d-flex"> <div class="toast-body"> Permalink information copied to clipboard </div> <button type="button" class="btn-close btn-close-white me-2 m-auto" data-bs-dismiss="toast" aria-label="Close"></button> </div> </div> </div> <div class="abody"> <div id="reference-panel" class="d-none"> <a class="close" href="#"><span class="bi bi-x-circle"></span></a> <div id="reference-panel-item"></div> </div> </div> </main> <footer class="mt-auto"> <div class="mt-5"></div> <div class="line"></div> <div class="container text-center my-3 bannerZone"> <div class="row justify-content-md-center societyBanner"> <div class="banner col py-2"> <a target="_blank" href="http://www.asiansgo.org/"><img src="https://ejgo.org/image/banner_asgo.gif" border="0"></a> </div> <div class="banner col py-2"> <a target="_blank" href="https://jsgo.or.jp/"><img src="https://ejgo.org/image/banner_jsgo.png" border="0"></a> </div> <div class="banner col py-2"> <a target="_blank" href="http://www.sgo.or.kr/"><img src="https://ejgo.org/image/banner_ksgo.gif" border="0"></a> </div> <div class="banner col py-2"> <a target="_blank" href="https://gyneoncology.ca/"><img src="https://ejgo.org/image/banner_goc.jpg" border="0"></a> </div> </div> </div> <div class="line"></div> <div class="container text-center my-3 bannerZone"> <div class="adBannerViewPorion"> <div class="row justify-content-md-center adBanner"> <div class="banner col py-2 ad1"><a href="http://www.astrazeneca.co.kr/" target="_blank"><img src="https://ejgo.org/images/ad_astrazeneca_320x80.jpg" width="220" style="border: 1px solid black;"></a></div> <div class="banner col py-2 ad2"><a href="https://www.msd-korea.com/" target="_blank"><img src="https://ejgo.org/images/ad-msd.jpg" border="0" width="220"></a></div> <div class="banner col py-2 ad3"><a href="http://www.hanmihealthcare.co.kr/home/main.hm" target="_blank"><img src="https://ejgo.org/images/ad-hamihealth.jpg" border="0" width="220"></a></div> <div class="banner col py-2 ad4"><a href="https://www.hpv16and18.com" target="_blank"><img src="https://ejgo.org/images/ad-cobas.jpg" width="220"></a></div>  </div> </div> </div> <div class="line"></div> <div class="container px-4 text-center footer-text footer-data"> <div class="row row-cols-md-2 row-cols-2 row-cols-lg-4 g-2 g-lg-3"> <div class="col p-3"> <b>Publisher</b> <a style="padding-left: 10px; text-indent: -10px;" href="http://www.asiansgo.org/" target="_blank">Asian Society of Gynecologic Oncology</a> <a style="padding-left: 10px; text-indent: -10px;" href="http://www.sgo.or.kr/" target="_blank">Korean Society of Gynecologic Oncology</a> <a style="padding-left: 10px; text-indent: -10px;" href="https://jsgo.or.jp/" target="_blank">Japan Society of Gynecologic Oncology</a>  <div style="padding-left: 10px; text-indent: -10px;" class="mt-3 text-start">Journal of Gynecologic Oncology</div> <div class="text-start">✉ <a class="d-inline" href="mailto://jgo@ejgo.org" target="_blank">jgo@ejgo.org</a></div> <a class="mt-3" style="color: #a0a0a0; FONT-SIZE: 100%; font-weight: bold; font-style: italic;" href="index.php?body=openaccess">Open Access</a> </div> <div class="col p-3"> <b>JGO on</b> <a href="https://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&term=%22J%20Gynecol%20Oncol%22%5BJournal%5D" target="_blank">PubMed</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/journals/883/" target="_blank">PubMed Central</a> <a href="http://webofknowledge.com/" target="_blank">Web of Science</a> <a href="https://www.scopus.com/" target="_blank">Scopus</a> <a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Journal+of+Gynecologic+Oncology&btnG=" target="_blank">Google Scholar</a> <a href="https://www.crossref.org/" target="_blank">Crossref</a> </div> <div class="col p-3"> <b>Information</b> <a href="index.php?body=aims">Aims and Scope</a> <a href="index.php?body=board">Editorial Board</a> <a href="index.php?body=instructions">Instructions for Authors</a> <a href="index.php?body=j-info">Journal Data</a> </div> <div class="col p-3"> <b>Browse</b> <a href="index.php?body=forthcoming">Online First Articles</a> <a href="index.php?body=current">Current Issue</a> <a href="index.php?body=archive">All Issues</a> <a class="mt-3" href="index.php?body=video-articles">Articles by Categories</a> </div> </div> </div> <div class="container-fluid"> <div class="bottom row"> <div> <a href="https://xmlink.kr/XMLink-Hub.php" target="_blank" style="color: #BBBBBB;"><img src="https://xmlink.kr/images/xmlinkhub-logo-gray-2024.png" style="margin-right: 50px;">© 2025 </a> </div> </div> </div>   </flooter>  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.6.4/jquery.min.js"></script> <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/js/bootstrap.bundle.min.js" integrity="sha384-YvpcrYf0tY3lHB60NNkmXc5s9fDVZLESaAA55NDzOxhy9GkcIdslK1eN7N6jIeHz" crossorigin="anonymous"></script> <script src="https://cdn.jsdelivr.net/gh/kartik-v/bootstrap-popover-x@1.5.1/js/bootstrap-popover-x.min.js" type="text/javascript"></script> <script src="js/jquery.twbsPagination.min.js" type="text/javascript"></script> <script src="js/nouislider.min.js" type="text/javascript"></script> <script src="js/wNumb.min.js" type="text/javascript"></script> <script src="js/chartist.min.js" type="text/javascript"></script> <script src="js/chartist-plugin-tooltip.js" type="text/javascript"></script> <script src="js/commonScript.js" type="text/javascript"></script> <script src="js/sharepopoverScript.js" type="text/javascript"></script> <script src="js/refpopoverScript.js" type="text/javascript"></script> <script> jQuery(document).ready(function ($) { // Tooltip const tooltipTriggerList = document.querySelectorAll('[data-bs-toggle="tooltip"]'); const tooltipList = [...tooltipTriggerList].map(tooltipTriggerEl => new bootstrap.Tooltip(tooltipTriggerEl)); $("#SharePermalinkText").val("https://ejgo.org/DOIx.php?id=10.3802/jgo.2025.36.e26"); // Icons const articleIconCoressp = document.getElementsByClassName("article-icon-corresp"); for (let i = 0; i < articleIconCoressp.length; i++) { articleIconCoressp[i].classList.add('bi', 'bi-envelope' ); } // Document Type document.getElementsByClassName("article-type")[0].innerHTML = "Review Article"; // // Relate Articles // const aRelateArticleInfo = []; const articleRelateNode = document.getElementById("article-relate-articles"); if ( articleRelateNode != null ) { articleRelateNode.classList.add('list-group', 'my-3'); for (index in aRelateArticleInfo) { let div = document.createElement('div'); div.setAttribute('class', 'list-group-item'); div.innerHTML = aRelateArticleInfo[ index ]; articleRelateNode.appendChild ( div ); } } // // SCRIPTS // // // for Authors and Keywords // // Function to transform keywords into links function transformKeywords() { var keywordTitles = document.querySelectorAll('.article-keyword-group-title'); keywordTitles.forEach(function (keywordTitle) { var keywordContainer = keywordTitle.nextElementSibling; if (keywordContainer) { var keywordElements = keywordContainer.querySelectorAll('.capture-id'); keywordElements.forEach(function (element) { var keyword = element.textContent.trim(); var link = document.createElement('a'); link.href = '/search.php?where=asummary&queryfilter=keyword&query=' + encodeURIComponent(keyword); link.textContent = keyword; element.innerHTML = ''; element.appendChild(link); element.classList.add('keyword-group-item'); }); } }); } // transformKeywords(); var articleContrib = document.querySelector('.article-contrib'); var nodes = articleContrib.childNodes; if ($(".article-contrib .capture-id").length > 6) { for (var i = 0; i < nodes.length; i++) { if (nodes[i].nodeType === Node.TEXT_NODE && nodes[i].nodeValue.trim() === 'and') { var span = document.createElement('span'); span.style.display = 'none'; span.className = 'hidden-and-text'; span.textContent = ' and '; articleContrib.replaceChild(span, nodes[i]); break; } } } function applyShowMore(selector) { $(selector + " .capture-id").each(function (index) { if (index > 5) { $(this).hide(); } }); if ($(selector + " .capture-id").length > 6) { $(selector).append('<a href="#" class="show-more show-toggle">view all</a>'); } $(selector).on("click", ".show-more", function (e) { e.preventDefault(); $(selector + " .capture-id:hidden").show(); if (selector === ".article-contrib") { $(".hidden-and-text").show(); } $(this).text(" view less ").removeClass("show-more").addClass("show-less"); }); $(selector).on("click", ".show-less", function (e) { e.preventDefault(); $(selector + " .capture-id").slice(6).hide(); if (selector === ".article-contrib") { $(".hidden-and-text").hide(); } $(this).text("view all").removeClass("show-less").addClass("show-more"); }); } applyShowMore(".article-contrib"); applyShowMore(".article-contrib-ko"); // // for SECTIONS // var aArticleSection = new Array(); $( ".__SECTION__" ).each(function( index, elem ){ aArticleSection.push( $(elem).attr('data-section-name') ); }); $( ".__SECTION__" ).each(function( index, elem ){ var $dropdownMenuNode = $(elem).children(".dropdown").find(".dropdown-menu"); $( aArticleSection ).each(function( index, elem ){ $dropdownMenuNode.append('<a class="dropdown-item" href="#__ID_SECTION_'+index+'">'+ elem +'</a>'); }); }); $( aArticleSection ).each(function( index, elem ){ $( '#navbar-goto-item' ).append('<li class="nav-item"><a class="nav-link" href="#__ID_SECTION_'+index+'">'+ elem +'</a></li>'); $( '#navbar-article-top-toolbar-goto' ).append('<li><a class="dropdown-item" href="#__ID_SECTION_'+index+'">'+ elem +'</a></li>'); }); function moveArticleToSection( targetElement ) { var headerHeight = parseInt(getComputedStyle(document.querySelector('header')).height); var navbarArticleTopToolbarHeight = parseInt(getComputedStyle(document.querySelector('#navbar-article-top-toolbar')).height); var scrollDirection = targetElement.offsetTop > window.pageYOffset ? navbarArticleTopToolbarHeight + 15 : headerHeight + 15; window.scrollTo({ top: targetElement.offsetTop - scrollDirection , behavior: 'auto' }); } document.querySelectorAll('#navbar-article-top-toolbar-goto a[href^="#__ID_SECTION_"]').forEach(anchor => { anchor.addEventListener('click', function (e) { e.preventDefault(); const targetElement = document.querySelector(this.getAttribute('href')); moveArticleToSection ( targetElement ); }); }); const moveArticleToSectionNode = document.querySelector('#article-move-supplement-material'); if ( moveArticleToSectionNode != null ) { moveArticleToSectionNode.addEventListener('click', function (e) { e.preventDefault(); const targetElement = document.querySelector(moveArticleToSectionNode.getAttribute('href')); moveArticleToSection ( targetElement ); }); } const scrollSpyTop = new bootstrap.ScrollSpy(document.body, { target: '#navbar-article-top-toolbar', }); const scrollSpyPortlet = new bootstrap.ScrollSpy(document.body, { target: '#navbar-goto', }); if ( aArticleSection.length > 1 ) { $(".navbar-goto-display").removeClass("d-none"); } // // for CROSSMARK // $( "#crossref-crossmark a" ).append('<img src="https://crossmark-cdn.crossref.org/widget/v2.0/logos/CROSSMARK_Color_square.svg" width="60" />'); $( "#crossref-crossmark" ).show(); // // // var aPageViewCount = { labels: ['Jul/2024','Aug/2024','Sep/2024','Oct/2024','Nov/2024','Dec/2024','Jan/2025','Feb/2025'], series: [ [{meta:'Jul/2024', value: 11},{meta:'Aug/2024', value: 28},{meta:'Sep/2024', value: 25},{meta:'Oct/2024', value: 28},{meta:'Nov/2024', value: 17},{meta:'Dec/2024', value: 8},{meta:'Jan/2025', value: 19},{meta:'Feb/2025', value: 17}], ] }; var aPDFDownloadCount = { labels: ['Jul/2024','Aug/2024','Sep/2024','Oct/2024','Nov/2024','Dec/2024','Jan/2025','Feb/2025'], series: [ [{meta:'Jul/2024', value: 1},{meta:'Aug/2024', value: 7},{meta:'Sep/2024', value: 5},{meta:'Oct/2024', value: 3},{meta:'Nov/2024', value: 2},{meta:'Dec/2024', value: 4},{meta:'Jan/2025', value: 5},{meta:'Feb/2025', value: 0}], ] }; $(".PageViewLink").on("click", function(){ new Chartist.Line('#my-chart', aPageViewCount, { plugins: [ Chartist.plugins.tooltip({ pointClass: 'ct-point', tooltipOffset: { x: 15, y: 35 }, transformTooltipTextFnc: function(value){ return 'Counts: ' + value; } }) ], fullWidth: false, lineSmooth: Chartist.Interpolation.cardinal({ tension: 0 }), low: 0, showArea: true, axisY: { onlyInteger: true, } }); $('#myModalLabel').html('Page Views'); $('#myModalTotalLinkLabel').html('<div class="d-flex">\ <div class="flex-grow-1">Total Page Views: 153</div><div><a href="/export/count/view/article/10.3802/jgo.2025.36.e26"><img src="image/icon-excel.png"> Download as Excel file</a></div>\ </div>\ '); var myModal = new bootstrap.Modal(document.getElementById('myModal')); myModal.show(); return false; }); $(".PDFDownloadLink").on("click", function(){ new Chartist.Line('#my-chart', aPDFDownloadCount, { fullWidth: false, low: 0, lineSmooth: Chartist.Interpolation.cardinal({ tension: 0 }), showArea: true, axisY: { onlyInteger: true, }, plugins: [ Chartist.plugins.tooltip({ pointClass: 'ct-point', tooltipOffset: { x: 15, y: 35 }, transformTooltipTextFnc: function(value){ return 'Counts: ' + value; } }) ], }); $('#myModalLabel').html('PDF Downloads'); $('#myModalTotalLinkLabel').html('<div class="d-flex"><div class="flex-grow-1">Total PDF Downloads: 27</div><div><a href="/export/count/download/pdf/10.3802/jgo.2025.36.e26"><img src="image/icon-excel.png"> Download as Excel file</a></div></div>'); var myModal = new bootstrap.Modal(document.getElementById('myModal')); myModal.show(); return false; }); $("#item-list li").click(function(e) { e.preventDefault(); e.stopPropagation(); }); $('.bm-ref-list-ol > li').css("cursor", "pointer"); //addClass('link'); $(".bm-ref-list-ol li").on( "click", function() { return false; }).children().click(function(e) { return false; }); }); var trackDownloadLink = function(category, doi) { gtag('event', 'download', { 'event_category': category, 'event_label': doi }); } </script> <script src="https://crossmark-cdn.crossref.org/widget/v2.0/widget.js" type="text/javascript"></script> <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script> <script type="text/javascript"> //pop up function notice_getcookie( name ){ var nameOfcookie = name + "="; var x = 0; while ( x <= document.cookie.length ) { var y = (x+nameOfcookie.length); if ( document.cookie.substring( x, y ) == nameOfcookie ) { if ( (endOfcookie=document.cookie.indexOf( ";", y )) == -1 ) endOfcookie = document.cookie.length; return unescape( document.cookie.substring( y, endOfcookie ) ); } x = document.cookie.indexOf( " ", x ) + 1; if ( x == 0 ) break; } return ""; } if ( notice_getcookie( "Notice" ) != "done" ) { popup1=window.open("jgo-notice-202408.html","popup1","scrollbars=0,width=637,height=470,left=100,top=100") } </script> <script src="js/jgo-chart.js" type="text/javascript"></script> <script src="js/ga_modal.js" type="text/javascript"></script> <script src="js/article_modal.js" type="text/javascript"></script> <script src="js/scroll_change_menu.js"></script> </body> </html>

CINXE.COM

:: JGO :: Journal of Gynecologic Oncology