{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T18:38:21Z","timestamp":1793126301368,"version":"4.5.0"},"reference-count":58,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"9","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) tools could improve clinical decision making or exacerbate inequities because of bias. African American (AA) men reportedly have a worse prognosis for prostate cancer (PCa) and are underrepresented in the development genomic biomarkers. We assess the generalizability of tools developed using a multimodal AI (MMAI) deep learning system using digital histopathology and clinical data from NRG\/Radiation Therapy Oncology Group PCa trials across racial subgroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>In total, 5,708 patients from five randomized phase III trials were included. Two MMAI algorithms were evaluated: (1) the distant metastasis (DM) MMAI model optimized to predict risk of DM, and (2) the PCa-specific mortality (PCSM) MMAI model optimized to focus on prediction death in the presence of DM (DDM). The prognostic performance of the MMAI algorithms was evaluated in AA and non-AA subgroups using time to DM (primary end point) and time to DDM (secondary end point). Exploratory end points included time to biochemical failure and overall survival with Fine-Gray or Cox proportional hazards models. Cumulative incidence estimates were computed for time-to-event end points and compared using Gray's test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      There were 948 (16.6%) AA patients, 4,731 non-AA patients (82.9%), and 29 (0.5%) patients with unknown or missing race status. The DM-MMAI algorithm showed a strong prognostic signal for DM in the AA (subdistribution hazard ratio [sHR], 1.2 [95% CI, 1.0 to 1.3];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .007) and non-AA subgroups (sHR, 1.4 [95% CI, 1.3 to 1.5];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001). Similarly, the PCSM-MMAI score showed a strong prognostic signal for DDM in both AA (sHR, 1.3 [95% CI, 1.1 to 1.5];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .001) and non-AA subgroups (sHR, 1.5 [95% CI, 1.4 to 1.6];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001), with similar distributions of risk.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>Using cooperative group data sets with a racially diverse population, the MMAI algorithm performed well across racial subgroups without evidence of algorithmic bias.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-24-00284","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T16:00:21Z","timestamp":1746806421000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":12,"title":["Assessing Algorithmic Fairness With a Multimodal Artificial Intelligence Model in Men of African and Non-African Origin on NRG Oncology Prostate Cancer Phase III Trials"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3546-8565","authenticated-orcid":true,"suffix":"III","given":"Mack","family":"Roach","sequence":"first","affiliation":[{"name":"UCSF Medical Center, San Francisco, CA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingbin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Artera, Santa Barbara, 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