{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T11:27:22Z","timestamp":1786706842312,"version":"build-2736575974"},"reference-count":59,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Prediction models can contribute to disparities in care by performing unequally across demographic groups. While fairness\u2010aware methods have been explored for binary outcomes, applications to survival analysis remain limited. This study compares two fairness\u2010aware deep learning survival models to mitigate racial disparities in predicting survival after radical prostatectomy for prostate cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used the National Cancer Database to train deep Cox proportional hazards models for overall survival. Two fairness\u2010aware approaches, Fair Deep Cox Proportional Hazards Model (Fair DCPH) and Group Distributionally Robust Optimization Deep Cox Proportional Hazards Model (GroupDRO DCPH), were compared against a standard Deep Cox model (Baseline). Model fairness was assessed via cross\u2010group and within\u2010group concordance indices (C\u2010index).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 418,968 included patients, 78.5% were White, with smaller proportions of Black (13.2%), Hispanic (4.5%), Asian (1.9%), and Other (2.0%) patients. The baseline DCPH model achieved a cross\u2010group C\u2010index of 0.699 for White patients but showed reduced performance for Black (0.678) and Hispanic (0.689) patients. Fairness\u2010aware models improved cross\u2010group C\u2010indices; for Black patients, cross\u2010group C\u2010index increased to 0.692 (Fair DCPH) and 0.696 (GroupDRO DCPH); for Hispanic patients, to 0.693 and 0.697, respectively. Cross\u2010group C\u2010index also improved in the Asian subgroup, where the C\u2010index rose from 0.696 (Baseline DCPH) to 0.702 (Fair DCPH) and 0.707 (GroupDRO DCPH), with minimal performance loss observed for White patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We benchmark two fairness\u2010aware survival models that address racial disparities in post\u2010prostatectomy survival prediction. These methods can be extended to other time\u2010to\u2010event models to ensure equitable care supported by fair prediction models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71544","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T08:13:54Z","timestamp":1769501634000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mitigating Disparities in Prostate Cancer Survival Prediction Through Fairness\u2010Aware Machine Learning Models"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5317-6809","authenticated-orcid":false,"given":"Hyungrok","family":"Do","sequence":"first","affiliation":[{"name":"Department of Population Health New York University School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajesh","family":"Ranganath","sequence":"additional","affiliation":[{"name":"Center for Data Science New York University  New York New York USA"},{"name":"Courant Institute of Mathematical Sciences New York University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katie","family":"Murray","sequence":"additional","affiliation":[{"name":"Department of Urology, New York University School of Medicine  New York New York USA"},{"name":"Department of Urology, Bellevue Hospital New York City Health and Hospitals  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7502-3475","authenticated-orcid":false,"given":"Madhur","family":"Nayan","sequence":"additional","affiliation":[{"name":"Department of Population Health New York University School of Medicine  New York New York USA"},{"name":"Department of Urology, New York University School of Medicine  New York New York USA"},{"name":"Department of Urology, Bellevue Hospital New York City Health and Hospitals  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21871"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/JU.0000000000002757"},{"issue":"6","key":"e_1_2_10_4_1","article-title":"Radical Prostatectomy Versus Deferred Treatment for Localised Prostate Cancer","volume":"6","author":"Vernooij R. 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