{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T22:01:55Z","timestamp":1793916115232,"version":"4.8.0"},"reference-count":25,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"7","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Precis Oncol"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Metastatic hormone-sensitive prostate cancer (mHSPC) is a heterogeneous disease state with multiple treatment options. Biomarkers are needed for risk stratification and to guide treatment intensification or deintensification. We used a computational histology artificial intelligence\u2013based platform (CHAI) to develop and validate a digital image\u2013only prognostic biomarker in mHSPC with participant-level data from two prospective, phase III randomized controlled trials.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>The CHAI platform extracted quantitative histomorphologic features from whole-slide images of hematoxylin and eosin\u2013stained diagnostic specimens. Data from CHAARTED were used to construct a signature of features associated with overall survival (OS). A continuous risk score was dichotomized into favorable- and unfavorable-risk groups. The performance of the locked model was assessed in ENZAMET as an independent validation cohort using Kaplan-Meier methods and multivariable Cox proportional hazards models. Institutional review board approval was obtained for each participating data set. Given all patient information was deidentified, the study was considered institutional review board\u2013exempt and consent waived.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      Overall, 1,191 participants were included: 507 in development (CHAARTED) and 684 in validation (ENZAMET). In the validation cohort, CHAI unfavorable-risk participants had worse OS (hazard ratio [HR], 2.6 [95% CI, 2.0 to 3.4];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001) and progression-free survival (PFS; HR, 2.2 [95% CI, 1.7 to 2.7];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001). Five-year OS was 39% for unfavorable-risk versus 69% for favorable-risk participants. In adjusting for clinical variables, the biomarker remained prognostic for OS and PFS (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001). Among those who received doublet (androgen-deprivation therapy [ADT] plus androgen receptor pathway inhibitor [ARPI]) versus triplet systemic therapy (ADT plus docetaxel plus ARPI), there was a significant interaction between biomarker and treatment intensification for OS (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .003) and PFS (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001), suggesting that patients at higher risk may benefit more from triplet therapy.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>We developed and validated an image-only AI-based biomarker associated with clinical outcomes and potential benefit from treatment escalation in mHSPC independent of conventional clinicopathologic risk factors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/po-26-00321","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T19:59:12Z","timestamp":1783540752000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":5,"title":["Development and Validation of a Computational Histology Artificial Intelligence\u2013Powered Biomarker in Metastatic Hormone-Sensitive Prostate Cancer on Randomized Phase III Trials"],"prefix":"10.1200","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0398-6018","authenticated-orcid":true,"given":"Christopher J.","family":"Sweeney","sequence":"first","affiliation":[{"name":"South Australian Immunogenomics Cancer Institute, Adelaide University, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5927-807X","authenticated-orcid":true,"given":"Vrishab","family":"Krishna","sequence":"additional","affiliation":[{"name":"Valar Labs, Palo 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