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<!DOCTYPE html> <html lang="en" class="no-js"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes"> <title>Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion</title> <meta id="meta-title" property="citation_title" content="Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion"/> <meta id="og-title" property="og:title" content="Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion"/> <meta name="twitter:widgets:autoload" content="off"/> <meta name="twitter:dnt" content="on"/> <meta name="twitter:widgets:csp" content="on"/> <meta name="google-site-verification" content="lQbRRf0vgPqMbnbCsgELjAjIIyJjiIWo917M7hBshvI"/> <meta id="meta-abstract" name="citation_abstract" content="BackgroundEarly identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.MethodsIn this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.ResultsFrom a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P &lt; .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR).ConclusionAddition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma."/> <meta id="meta-description" name="description" content="Author(s): Kihira, Shingo; Tsankova, Nadejda; Bauer, Adam; Sakai, Yu; Mahmoudi, Keon; Zubizarreta, Nicole; Houldsworth, Jane; Khan, Fahad; Salamon, Noriko; Hormigo, Adilia; Nael, Kambiz | Abstract: BackgroundEarly identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.MethodsIn this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.ResultsFrom a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P &lt; .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR).ConclusionAddition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma."/> <meta id="og-description" name="og:description" content="Author(s): Kihira, Shingo; Tsankova, Nadejda; Bauer, Adam; Sakai, Yu; Mahmoudi, Keon; Zubizarreta, Nicole; Houldsworth, Jane; Khan, Fahad; Salamon, Noriko; Hormigo, Adilia; Nael, Kambiz | Abstract: BackgroundEarly identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.MethodsIn this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.ResultsFrom a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P &lt; .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR).ConclusionAddition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma."/> <meta id="og-image" property="og:image" content="https://escholarship.org/images/escholarship-facebook2.jpg"/> <meta id="og-image-width" property="og:image:width" content="1242"/> <meta id="og-image-height" property="og:image:height" content="1242"/> <meta id="meta-author0" name="citation_author" content="Kihira, Shingo"/> <meta id="meta-author1" name="citation_author" content="Tsankova, Nadejda"/> <meta id="meta-author2" name="citation_author" content="Bauer, Adam"/> <meta id="meta-author3" name="citation_author" content="Sakai, Yu"/> <meta id="meta-author4" name="citation_author" content="Mahmoudi, Keon"/> <meta id="meta-author5" name="citation_author" content="Zubizarreta, Nicole"/> <meta id="meta-author6" name="citation_author" content="Houldsworth, Jane"/> <meta id="meta-author7" name="citation_author" content="Khan, Fahad"/> <meta id="meta-author8" name="citation_author" content="Salamon, Noriko"/> <meta id="meta-author9" name="citation_author" content="Hormigo, Adilia"/> <meta id="meta-author10" name="citation_author" content="Nael, Kambiz"/> <meta id="meta-publication_date" name="citation_publication_date" content="2021"/> <meta id="meta-doi" name="citation_doi" content="10.1093/noajnl/vdab051"/> <meta id="meta-journal_title" name="citation_journal_title" content="Neuro-Oncology Advances"/> <meta id="meta-issn" name="citation_issn" content="0801-3284"/> <meta id="meta-volume" name="citation_volume" content="3"/> <meta id="meta-issue" name="citation_issue" content="1"/> <meta id="meta-firstpage" name="citation_firstpage" content="vdab051-"/> <meta id="meta-online_date" name="citation_online_date" content="2021-09-20"/> <meta id="meta-pdf_url" name="citation_pdf_url" content="https://escholarship.org/content/qt80s7v9kh/qt80s7v9kh.pdf?t=qzpyy6"/> <link rel="canonical" href="https://escholarship.org/uc/item/80s7v9kh"/> <link rel="stylesheet" href="/css/main-62e3023ddd136de2.css"> <link rel="resource" type="application/l10n" href="/node_modules/pdfjs-embed2/dist/locale/locale.properties"> <noscript><style> .jsonly { display: none } </style></noscript>   <script> var _paq = window._paq = window._paq || []; 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</li><li><a href="/search/?q=author%3ATsankova%2C%20Nadejda">Tsankova, Nadejda</a>; </li><li><a href="/search/?q=author%3ABauer%2C%20Adam">Bauer, Adam</a>; </li><li><a href="/search/?q=author%3ASakai%2C%20Yu">Sakai, Yu</a>; </li><li><a href="/search/?q=author%3AMahmoudi%2C%20Keon">Mahmoudi, Keon</a>; </li><li><a href="/search/?q=author%3AZubizarreta%2C%20Nicole">Zubizarreta, Nicole</a>; </li><li><a href="/search/?q=author%3AHouldsworth%2C%20Jane">Houldsworth, Jane</a>; </li><li><a href="/search/?q=author%3AKhan%2C%20Fahad">Khan, Fahad</a>; </li><li><a href="/search/?q=author%3ASalamon%2C%20Noriko">Salamon, Noriko</a>; </li><li><a href="/search/?q=author%3AHormigo%2C%20Adilia">Hormigo, Adilia</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ANael%2C%20Kambiz">Nael, Kambiz</a> </li><li><a class="c-authorlist__list-more-link">et al.</a></li></ul></div><div class="c-pubinfo"><h2 class="c-pubinfo__location-heading">Published Web Location</h2><a href="https://doi.org/10.1093/noajnl/vdab051" class="c-pubinfo__link">https://doi.org/10.1093/noajnl/vdab051</a></div><div class="c-tabs"><div class="c-tabs__tabs"><button class="c-tabs__button-more" aria-label="Show all tabs">...</button><button class="c-tabs__button--active">Main Content</button><button class="c-tabs__button">Metrics</button><button class="c-tabs__button">Author & Article Info</button></div><div class="c-tabs__content"><div class="c-tabcontent"><a name="article_abstract"></a><details class="c-togglecontent" open=""><summary>Abstract</summary><div class="c-clientmarkup"><p><h3>Background</h3>Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.<h3>Methods</h3>In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known <i>IDH1</i>, <i>EGFR</i>, <i>MGMT</i>, <i>ATRX</i>, <i>TP53</i>, and <i>PTEN</i> status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.<h3>Results</h3>From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for <i>IDH1</i>, 0.99/80% for <i>ATRX</i>, 0.79/67% for <i>MGMT</i>, and 0.77/66% for <i>EGFR</i>. The addition of diffusion data to conventional MRI features significantly (<i>P</i> < .05) increased predictive performance for <i>IDH1</i>, <i>MGMT</i>, and <i>ATRX</i>. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (<i>IDH1</i>), 70% (<i>ATRX</i>), 70% (<i>MGMT</i>), and 75% (<i>EGFR</i>).<h3>Conclusion</h3>Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.</p></div><p class="o-well-colored">Many UC-authored scholarly publications are freely available on this site because of the UC's <a href="https://osc.universityofcalifornia.edu/open-access-at-uc/open-access-policy/">open access policies</a>. <a href="https://help.escholarship.org/support/tickets/new">Let us know how this access is important for you.</a></p></details><details class="c-togglecontent" open=""><a name="article_main"></a><summary>Main Content</summary><div class="c-pdfview"><button class="c-pdfview__button-download">Download PDF to View</button><button class="c-pdfview__button-view">View Larger</button></div><div 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patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.<h4>Methods</h4>In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known <i>IDH1</i>, <i>EGFR</i>, <i>MGMT</i>, <i>ATRX</i>, <i>TP53</i>, and <i>PTEN</i> status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.<h4>Results</h4>From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for <i>IDH1</i>, 0.99/80% for <i>ATRX</i>, 0.79/67% for <i>MGMT</i>, and 0.77/66% for <i>EGFR</i>. The addition of diffusion data to conventional MRI features significantly (<i>P</i> < .05) increased predictive performance for <i>IDH1</i>, <i>MGMT</i>, and <i>ATRX</i>. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (<i>IDH1</i>), 70% (<i>ATRX</i>), 70% (<i>MGMT</i>), and 75% (<i>EGFR</i>).<h4>Conclusion</h4>Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.","keywords":["Biomedical and Clinical Sciences","Clinical Sciences","Oncology and Carcinogenesis","Cancer","Clinical Research","Neurosciences","Brain Disorders","Rare Diseases","Brain Cancer","Biomedical Imaging","Detection","screening and diagnosis","4.2 Evaluation of markers and technologies","glioma","MR diffusion","multiparametric MRI","radiogenomics","texture 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Fahad","fname":"Fahad","lname":"Khan"},{"name":"Salamon, Noriko","email":"nsalamon@mednet.ucla.edu","fname":"Noriko","lname":"Salamon","ORCID_id":"0000-0002-3520-9467"},{"name":"Hormigo, Adilia","fname":"Adilia","lname":"Hormigo"},{"name":"Nael, Kambiz","email":"kanael@mednet.ucla.edu","fname":"Kambiz","lname":"Nael","ORCID_id":"0000-0002-4194-9488"}],"citation":{"id":"qt80s7v9kh","type":"article","title":"Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion","URL":"http://escholarship.org/uc/item/80s7v9kh","issued":{"raw":["2021-1-1"]},"doi":"10.1093/noajnl/vdab051","issn":null,"author":[{"literal":"Kihira, Shingo","given":"Shingo","family":"Kihira"},{"literal":"Tsankova, Nadejda","given":"Nadejda","family":"Tsankova"},{"literal":"Bauer, Adam","given":"Adam","family":"Bauer"},{"literal":"Sakai, Yu","given":"Yu","family":"Sakai"},{"literal":"Mahmoudi, Keon","given":"Keon","family":"Mahmoudi"},{"literal":"Zubizarreta, Nicole","given":"Nicole","family":"Zubizarreta"},{"literal":"Houldsworth, Jane","given":"Jane","family":"Houldsworth"},{"literal":"Khan, Fahad","given":"Fahad","family":"Khan"},{"literal":"Salamon, Noriko","email":"nsalamon@mednet.ucla.edu","given":"Noriko","family":"Salamon","ORCID_id":"0000-0002-3520-9467"},{"literal":"Hormigo, Adilia","given":"Adilia","family":"Hormigo"},{"literal":"Nael, Kambiz","email":"kanael@mednet.ucla.edu","given":"Kambiz","family":"Nael","ORCID_id":"0000-0002-4194-9488"}]},"content_html":false,"content_key":"21024f858ab41b3aa37c148e97b8e8ff","content_type":"application/pdf","data_digest":"Y7mL8LgBqdgaf253WJpNrg==","editors":null,"genre":"article","id":"80s7v9kh","index_digest":"Cpt5bAwaiAMdSgk7RoB4lw==","last_indexed":"2023-10-10 14:59:34 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