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<!DOCTYPE html> <html lang="en" dir="ltr" xmlns="http://www.w3.org/1999/xhtml" xmlns:mml="http://www.w3.org/1998/Math/MathML"> <head prefix="og: http://ogp.me/ns# article: http://ogp.me/ns/article# book: http://ogp.me/ns/book#" >  <link rel="dns-prefetch" href="//d33xdlntwy0kbs.cloudfront.net" /> <link rel="dns-prefetch" href="//cdn.jsdelivr.net" /> <link rel="dns-prefetch" href="//www.google.com" /> <link rel="dns-prefetch" href="//scholar.google.com" /> <link rel="dns-prefetch" href="//www.googletagmanager.com" /> <link rel="dns-prefetch" href="//rum-static.pingdom.net" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="shortcut icon" href="https://www.medrxiv.org/sites/default/files/images/favicon.ico" type="image/vnd.microsoft.icon" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="type" content="article" /> <meta name="category" content="article" /> <meta name="HW.identifier" content="/medrxiv/early/2024/10/22/2024.10.22.24315007.atom" /> <meta name="HW.pisa" content="medrxiv;2024.10.22.24315007v1" /> <meta name="DC.Format" content="text/html" /> <meta name="DC.Language" content="en" /> <meta name="DC.Title" content="Performance of Handcrafted Radiomics versus Deep Learning for Prognosticating Head and Neck Squamous Cell Carcinoma – A Systematic Review with Critical Appraisal of Quantitative Imaging Studies" /> <meta name="DC.Identifier" content="10.1101/2024.10.22.24315007" /> <meta name="DC.Date" content="2024-10-22" /> <meta name="DC.Publisher" content="Cold Spring Harbor Laboratory Press" /> <meta name="DC.Rights" content="© 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/" /> <meta name="DC.AccessRights" content="restricted" /> <meta name="DC.Description" content="Head and neck squamous cell carcinoma (HNSCC) presents a complex clinical challenge due to its heterogeneous nature and diverse treatment responses. This systematic review critically appraises the performance of handcrafted radiomics (HC) and deep learning (DL) models in prognosticating outcomes in HNSCC patients treated with (chemo)-radiotherapy. A comprehensive literature search was conducted up to May 2023, identifying 23 eligible studies that met the inclusion criteria of methodological rigor and long-term outcome reporting. The review highlights the methodological variability and performance metrics of HC and DL models in predicting overall survival (OS), loco-regional recurrence (LRR) and distant metastasis (DM). While DL models demonstrated slightly superior performance metrics compared to HC models, the highest methodological quality was observed predominantly in studies using HC radiomics. The findings underscore the necessity for methodological improvements, including pre-registration of protocols and assessment of clinical utility, to enhance the reliability and applicability of radiomic-based prognostic models in clinical practice. ### Competing Interest Statement Andre Dekker is a founder, shareholder and employee of Medical Data Works B.V. All other authors do not have any competing interest. ### Funding Statement Authors acknowledge financial support from the NWO funded personal health Train for RAdiation oncology in India and the Netherlands: TRAIN project (629 002 212), the Stichting Hanarth Fonds and the Proton Therapy Research Infrastructure: ProTRAIT project (RUG 2017 8254) which is supported by the Dutch Cancer Society. Additionally, HMTT acknowledges the DBT/Wellcome Trust India Alliance Early Career Fellowship [Grant number: IA/E/18/1/504306] for the support. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This review was performed in accordance with the relevant ethical guidelines and regulations of the institution, and did not require any informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript." /> <meta name="DC.Contributor" content="Varsha Gouthamchand" /> <meta name="DC.Contributor" content="Louise AF Fonseca" /> <meta name="DC.Contributor" content="Frank JP Hoebers" /> <meta name="DC.Contributor" content="Rianne Fijten" /> <meta name="DC.Contributor" content="Andre Dekker" /> <meta name="DC.Contributor" content="Leonard Wee" /> <meta name="DC.Contributor" content="Hannah Mary Thomas T" /> <meta name="article:published_time" content="2024-10-22" /> <meta name="citation_title" content="Performance of Handcrafted Radiomics versus Deep Learning for Prognosticating Head and Neck Squamous Cell Carcinoma – A Systematic Review with Critical Appraisal of Quantitative Imaging Studies" /> <meta name="citation_abstract" lang="en" content="<h3>Abstract</h3> <p>Head and neck squamous cell carcinoma (HNSCC) presents a complex clinical challenge due to its heterogeneous nature and diverse treatment responses. This systematic review critically appraises the performance of handcrafted radiomics (HC) and deep learning (DL) models in prognosticating outcomes in HNSCC patients treated with (chemo)-radiotherapy. A comprehensive literature search was conducted up to May 2023, identifying 23 eligible studies that met the inclusion criteria of methodological rigor and long-term outcome reporting. The review highlights the methodological variability and performance metrics of HC and DL models in predicting overall survival (OS), loco-regional recurrence (LRR) and distant metastasis (DM). While DL models demonstrated slightly superior performance metrics compared to HC models, the highest methodological quality was observed predominantly in studies using HC radiomics. The findings underscore the necessity for methodological improvements, including pre-registration of protocols and assessment of clinical utility, to enhance the reliability and applicability of radiomic-based prognostic models in clinical practice.</p>" /> <meta name="citation_journal_title" content="medRxiv" /> <meta name="citation_publisher" content="Cold Spring Harbor Laboratory Press" /> <meta name="citation_publication_date" content="2024/01/01" /> <meta name="citation_mjid" content="medrxiv;2024.10.22.24315007v1" /> <meta name="citation_id" content="2024.10.22.24315007v1" /> <meta name="citation_public_url" content="https://www.medrxiv.org/content/10.1101/2024.10.22.24315007v1" /> <meta name="citation_abstract_html_url" content="https://www.medrxiv.org/content/10.1101/2024.10.22.24315007v1.abstract" /> <meta name="citation_full_html_url" content="https://www.medrxiv.org/content/10.1101/2024.10.22.24315007v1.full" /> <meta name="citation_pdf_url" content="https://www.medrxiv.org/content/medrxiv/early/2024/10/22/2024.10.22.24315007.full.pdf" /> <meta name="citation_doi" content="10.1101/2024.10.22.24315007" /> <meta name="citation_num_pages" content="25" /> <meta name="citation_article_type" content="Article" /> <meta name="citation_firstpage" content="2024.10.22.24315007" /> <meta name="citation_author" content="Varsha Gouthamchand" /> <meta name="citation_author_institution" content="Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+" /> <meta name="citation_author_email" content="varsha.gouthamchand@maastro.nl" /> <meta name="citation_author_orcid" content="http://orcid.org/0000-0002-4756-2866" /> <meta name="citation_author" content="Louise AF Fonseca" /> <meta name="citation_author_institution" content="Academic Center for General Practice, Department of Public Health and Primary Care" /> <meta name="citation_author" content="Frank JP Hoebers" /> <meta name="citation_author_institution" content="Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+" /> <meta name="citation_author" content="Rianne Fijten" /> <meta name="citation_author_institution" content="Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+" /> <meta name="citation_author_orcid" content="http://orcid.org/0000-0002-1964-6317" /> <meta name="citation_author" content="Andre Dekker" /> <meta name="citation_author_institution" content="Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+" /> <meta name="citation_author_institution" content="Brightlands Institute for Smart Society, Faculty of Science and Engineering, Maastricht University" /> <meta name="citation_author_orcid" content="http://orcid.org/0000-0002-0422-7996" /> <meta name="citation_author" content="Leonard Wee" /> <meta name="citation_author_institution" content="Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+" /> <meta name="citation_author_orcid" content="http://orcid.org/0000-0003-1612-9055" /> <meta name="citation_author" content="Hannah Mary Thomas T" /> <meta name="citation_author_institution" content="Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology, Unit II, Christian Medical College" /> <meta name="citation_reference" content="Johnson, D.E., et al., Head and neck squamous cell carcinoma. 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[cited 2024 August 25]; Available from: https://github.com/oncoray/cnn-hnscc." /> <meta name="twitter:title" content="Performance of Handcrafted Radiomics versus Deep Learning for Prognosticating Head and Neck Squamous Cell Carcinoma – A Systematic Review with Critical Appraisal of Quantitative Imaging Studies" /> <meta name="twitter:site" content="@medrxivpreprint" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:image" content="https://www.medrxiv.org/sites/default/files/images/medrxiv_logo_homepage7-5-small-test-up.png" /> <meta name="twitter:description" content="Head and neck squamous cell carcinoma (HNSCC) presents a complex clinical challenge due to its heterogeneous nature and diverse treatment responses. This systematic review critically appraises the performance of handcrafted radiomics (HC) and deep learning (DL) models in prognosticating outcomes in HNSCC patients treated with (chemo)-radiotherapy. A comprehensive literature search was conducted up to May 2023, identifying 23 eligible studies that met the inclusion criteria of methodological rigor and long-term outcome reporting. The review highlights the methodological variability and performance metrics of HC and DL models in predicting overall survival (OS), loco-regional recurrence (LRR) and distant metastasis (DM). While DL models demonstrated slightly superior performance metrics compared to HC models, the highest methodological quality was observed predominantly in studies using HC radiomics. The findings underscore the necessity for methodological improvements, including pre-registration of protocols and assessment of clinical utility, to enhance the reliability and applicability of radiomic-based prognostic models in clinical practice. ### Competing Interest Statement Andre Dekker is a founder, shareholder and employee of Medical Data Works B.V. All other authors do not have any competing interest. ### Funding Statement Authors acknowledge financial support from the NWO funded personal health Train for RAdiation oncology in India and the Netherlands: TRAIN project (629 002 212), the Stichting Hanarth Fonds and the Proton Therapy Research Infrastructure: ProTRAIT project (RUG 2017 8254) which is supported by the Dutch Cancer Society. Additionally, HMTT acknowledges the DBT/Wellcome Trust India Alliance Early Career Fellowship [Grant number: IA/E/18/1/504306] for the support. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This review was performed in accordance with the relevant ethical guidelines and regulations of the institution, and did not require any informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript." /> <meta name="og-title" property="og:title" content="Performance of Handcrafted Radiomics versus Deep Learning for Prognosticating Head and Neck Squamous Cell Carcinoma – A Systematic Review with Critical Appraisal of Quantitative Imaging Studies" /> <meta name="og-url" property="og:url" content="https://www.medrxiv.org/content/10.1101/2024.10.22.24315007v1" /> <meta name="og-site-name" property="og:site_name" content="medRxiv" /> <meta name="og-description" property="og:description" content="Head and neck squamous cell carcinoma (HNSCC) presents a complex clinical challenge due to its heterogeneous nature and diverse treatment responses. This systematic review critically appraises the performance of handcrafted radiomics (HC) and deep learning (DL) models in prognosticating outcomes in HNSCC patients treated with (chemo)-radiotherapy. A comprehensive literature search was conducted up to May 2023, identifying 23 eligible studies that met the inclusion criteria of methodological rigor and long-term outcome reporting. The review highlights the methodological variability and performance metrics of HC and DL models in predicting overall survival (OS), loco-regional recurrence (LRR) and distant metastasis (DM). While DL models demonstrated slightly superior performance metrics compared to HC models, the highest methodological quality was observed predominantly in studies using HC radiomics. The findings underscore the necessity for methodological improvements, including pre-registration of protocols and assessment of clinical utility, to enhance the reliability and applicability of radiomic-based prognostic models in clinical practice. ### Competing Interest Statement Andre Dekker is a founder, shareholder and employee of Medical Data Works B.V. All other authors do not have any competing interest. ### Funding Statement Authors acknowledge financial support from the NWO funded personal health Train for RAdiation oncology in India and the Netherlands: TRAIN project (629 002 212), the Stichting Hanarth Fonds and the Proton Therapy Research Infrastructure: ProTRAIT project (RUG 2017 8254) which is supported by the Dutch Cancer Society. Additionally, HMTT acknowledges the DBT/Wellcome Trust India Alliance Early Career Fellowship [Grant number: IA/E/18/1/504306] for the support. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This review was performed in accordance with the relevant ethical guidelines and regulations of the institution, and did not require any informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. 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class="highwire-article-citation highwire-citation-type-highwire-article node887718" data-node-nid="887718" id="node-887718--2615879048" data-pisa="medrxiv;2024.10.22.24315007v1" data-pisa-master="medrxiv;2024.10.22.24315007" data-apath="/medrxiv/early/2024/10/22/2024.10.22.24315007.atom" data-hw-author-tooltip-instance=""><div class="highwire-cite highwire-cite-highwire-article highwire-citation-medrxiv-article-top clearfix has-author-tooltip" > <h1 class="highwire-cite-title" id="page-title">Performance of Handcrafted Radiomics versus Deep Learning for Prognosticating Head and Neck Squamous Cell Carcinoma – A Systematic Review with Critical Appraisal of Quantitative Imaging Studies</h1> <div class="highwire-cite-authors" ><span class="highwire-citation-authors"><span class="highwire-citation-author first hw-author-orcid-logo-wrapper" data-delta="0"><a href="http://orcid.org/0000-0002-4756-2866" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Varsha</span> <span class="nlm-surname">Gouthamchand</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Louise AF</span> <span class="nlm-surname">Fonseca</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Frank JP</span> <span class="nlm-surname">Hoebers</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="3"><a href="http://orcid.org/0000-0002-1964-6317" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Rianne</span> <span class="nlm-surname">Fijten</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="4"><a href="http://orcid.org/0000-0002-0422-7996" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Andre</span> <span class="nlm-surname">Dekker</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="5"><a href="http://orcid.org/0000-0003-1612-9055" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Leonard</span> <span class="nlm-surname">Wee</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Hannah Mary</span> <span class="nlm-surname">Thomas T</span></span></span></div> <div class="highwire-cite-metadata" ><span class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">doi:</span> https://doi.org/10.1101/2024.10.22.24315007 </span></div> </div> <div id="hw-article-author-popups-node-887718--2615879048" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Varsha Gouthamchand </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+</span>, Maastricht, <span class='nlm-country'>The Netherlands</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Varsha%2BGouthamchand%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Gouthamchand%20V&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AVarsha%2BGouthamchand%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link"><a href="http://orcid.org/0000-0002-4756-2866" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Varsha Gouthamchand</a></li><li class="author-corresp-email-link last"><span>For correspondence: <a href="mailto:varsha.gouthamchand@maastro.nl" class="" data-icon-position="" data-hide-link-title="0">varsha.gouthamchand@maastro.nl</a></span></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Louise AF Fonseca </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>3</span><span class='nlm-institution'>Academic Center for General Practice, Department of Public Health and Primary Care</span>, KU Leuven, Leuven, <span class='nlm-country'>Belgium</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Louise%2BAF%2BFonseca%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Fonseca%20LA&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3ALouise%2BAF%2BFonseca%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Frank JP Hoebers </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+</span>, Maastricht, <span class='nlm-country'>The Netherlands</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Frank%2BJP%2BHoebers%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Hoebers%20FJ&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AFrank%2BJP%2BHoebers%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Rianne Fijten </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+</span>, Maastricht, <span class='nlm-country'>The Netherlands</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Rianne%2BFijten%2B" target="_blank" class="" data-icon-position="" 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Institute for Oncology and Reproduction, Maastricht University Medical Centre+</span>, Maastricht, <span class='nlm-country'>The Netherlands</span></div><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>Brightlands Institute for Smart Society, Faculty of Science and Engineering, Maastricht University</span>, Heerlen, <span class='nlm-country'>The Netherlands</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Andre%2BDekker%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Dekker%20A&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a 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data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2 class="">Abstract</h2><p id="p-2">Head and neck squamous cell carcinoma (HNSCC) presents a complex clinical challenge due to its heterogeneous nature and diverse treatment responses. This systematic review critically appraises the performance of handcrafted radiomics (HC) and deep learning (DL) models in prognosticating outcomes in HNSCC patients treated with (chemo)-radiotherapy. A comprehensive literature search was conducted up to May 2023, identifying 23 eligible studies that met the inclusion criteria of methodological rigor and long-term outcome reporting. The review highlights the methodological variability and performance metrics of HC and DL models in predicting overall survival (OS), loco-regional recurrence (LRR) and distant metastasis (DM). While DL models demonstrated slightly superior performance metrics compared to HC models, the highest methodological quality was observed predominantly in studies using HC radiomics. The findings underscore the necessity for methodological improvements, including pre-registration of protocols and assessment of clinical utility, to enhance the reliability and applicability of radiomic-based prognostic models in clinical practice.</p></div><h3>Competing Interest Statement</h3><p id="p-3">Andre Dekker is a founder, shareholder and employee of Medical Data Works B.V. All other authors do not have any competing interest.</p><h3>Funding Statement</h3><p id="p-4">Authors acknowledge financial support from the NWO funded personal health Train for RAdiation oncology in India and the Netherlands: TRAIN project (629 002 212), the Stichting Hanarth Fonds and the Proton Therapy Research Infrastructure: ProTRAIT project (RUG 2017 8254) which is supported by the Dutch Cancer Society. Additionally, HMTT acknowledges the DBT/Wellcome Trust India Alliance Early Career Fellowship [Grant number: IA/E/18/1/504306] for the support.</p><h3>Author Declarations</h3><p id="p-5">I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.</p><p id="p-6">Yes</p><p id="p-7">The details of the IRB/oversight body that provided approval or exemption for the research described are given below:</p><p id="p-8">This review was performed in accordance with the relevant ethical guidelines and regulations of the institution, and did not require any informed consent.</p><p id="p-9">I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.</p><p id="p-10">Yes</p><p id="p-11">I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).</p><p id="p-12">Yes</p><p id="p-13">I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.</p><p id="p-14">Yes</p><div class="section data-availability" id="sec-18"><h2 class="">Data Availability</h2><p id="p-73">All data produced in the present work are contained in the manuscript.</p></div><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-biorxiv-copyright" > <div class="pane-content"> <div class="field field-name-field-highwire-copyright 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