{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T13:46:56Z","timestamp":1790948816699,"version":"4.1.0"},"reference-count":47,"publisher":"Wiley","issue":"24","license":[{"start":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T00:00:00Z","timestamp":1766275200000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study developed a habitat\u2010based radiomics classifier from CT images to predict HPV status in oropharyngeal cancer (OPC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed pretreatment CT scans from 192 OPC patients. Tumor habitats were generated using a two\u2010level unsupervised clustering approach, and radiomics features were calculated from both intratumoral and habitat\u2010defined subregions. HPV ground truth was based on p16 IHC, and the ROSE algorithm was used to address class imbalance (85% HPV\u2010positive). LASSO regression was used for feature selection. We developed and compared three separate models for statistical performance to predict HPV status: habitat radiomics classifier, intratumoral radiomics classifier, and combined radiomics classifier (habitats and intratumoral). Classifier performance was assessed using area under the receiver operating characteristic curve (AUCROC), and SHAP (SHapley Additive Explanations) was utilized to interpret features contributing to classifier predictions. Kaplan\u2013Meier analysis was conducted to compare survival outcomes of HPV ground truth versus the radiomics classifier.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The habitat radiomics classifier significantly outperformed the intratumoral radiomics classifier, achieving AUCs of 0.970 (95% CI, 0.942\u20130.997) in the training cohort and 0.937 (95% CI, 0.843\u20131.00) in the test cohort. The combined radiomics classifier did not significantly improve performance. SHAP revealed that radiomic features of compact\/spherical shape and uniform texture were associated with HPV\u2010positive tumors, reflecting lower morphologic and textural heterogeneity than HPV\u2010negative tumors. For the survival analysis, the HPV habitat radiomics classifier is indistinguishable from HPV ground truth in predicting overall survival (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.52 for HPV\u2010negative ground truth vs. HPV\u2010negative classifier;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.75 for HPV\u2010positive ground truth vs. HPV\u2010positive classifier).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The habitat radiomics classifier provides an imaging\u2010based algorithm to predict HPV status in OPC by capturing spatial patterns of tumor heterogeneity that reflect biological differences between HPV\u2010positive and HPV\u2010negative tumors. This approach demonstrated strong performance, with additional support from SHAP\u2010based interpretability and Kaplan\u2013Meier survival analysis suggesting its potential clinical relevance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71481","type":"journal-article","created":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T17:00:41Z","timestamp":1766336441000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Habitat Radiomics Predict\n                    <scp>HPV<\/scp>\n                    Status in Oropharyngeal Cancer"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8713-0697","authenticated-orcid":false,"given":"Oya","family":"Altinok","sequence":"first","affiliation":[{"name":"Department of Cancer Epidemiology H. Lee Moffitt Cancer Center &amp; Research Institute  Tampa Florida USA"},{"name":"Institute of Biomedical Engineering Bogazici University  Istanbul Turkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghulam","family":"Rasool","sequence":"additional","affiliation":[{"name":"Department of Cancer Epidemiology H. Lee Moffitt Cancer Center &amp; Research Institute  Tampa Florida USA"},{"name":"Department of Machine Learning H. Lee Moffitt Cancer Center and Research Institute  Tampa Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asim","family":"Waqas","sequence":"additional","affiliation":[{"name":"Department of Cancer Epidemiology H. Lee Moffitt Cancer Center &amp; Research Institute  Tampa Florida USA"},{"name":"Department of Machine Learning H. Lee Moffitt Cancer Center and Research Institute  Tampa Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3241-3216","authenticated-orcid":false,"given":"Matthew B.","family":"Schabath","sequence":"additional","affiliation":[{"name":"Department of Cancer Epidemiology H. Lee Moffitt Cancer Center &amp; Research Institute  Tampa Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert","family":"Guvenis","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering Bogazici University  Istanbul 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