{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:18:32Z","timestamp":1788153512525,"version":"build-2803163510"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>\n                    Men with\n                    <jats:italic>de novo<\/jats:italic>\n                    metastatic prostate cancer (mPCa) face a poor but heterogeneous prognosis, yet interpretable, transparently reported diagnostic-time tools remain limited. Using the Surveillance, Epidemiology, and End Results (SEER) 17-registry database (2010\u20132020), we developed and temporally validated machine-learning models to predict fixed 3-year cancer-specific mortality (CSM) and overall mortality (OM) in newly diagnosed mPCa. From 42,691 exported cases, we defined a cohort of 41,057 (survival \u22651 month) and split it temporally into training (2010\u20132016,\n                    <jats:italic>n<\/jats:italic>\n                    \u00a0=\u00a021,584) and validation (2017\u20132020,\n                    <jats:italic>n<\/jats:italic>\n                    \u00a0=\u00a019,473) sets. Seven algorithms were benchmarked on an identical diagnostic-time feature space [age, year, race, marital status, prostate-specific antigen (PSA), Gleason, and metastatic sites], excluding treatment and American Joint Committee on Cancer (AJCC) T\/N; CatBoost was selected as the primary model. In temporal validation, it achieved an area under the curve (AUC) of 0.701 [95% confidence interval (CI) 0.693\u20130.708] for net CSM, a cause-specific quantity excluding other-cause deaths, not an absolute competing-risks cumulative incidence, and 0.704 (0.697\u20130.712) for OM, with good calibration (slopes 1.12 and 1.08) and positive decision-curve net benefit for flagging high-risk patients across roughly 30%\u201370% thresholds; discrimination was comparable across leading algorithms and to logistic regression. Temporal internal\u2013external cross-validation confirmed stable discrimination (pooled AUC 0.701\/0.703; 95% prediction intervals from 0.672 to 0.725); because diagnosis year is a model feature, this establishes stability within 2010\u20132020, and the model should not be applied to later diagnosis years without local recalibration. Full validation-set SHapley Additive exPlanations (SHAP) analysis identified age, Gleason score, PSA, diagnosis year, and metastatic sites as dominant predictors, and training-derived risk tertiles separated validation event rates monotonically (net CSM 28%\/49%\/67%; OM 34%\/57%\/74%). Transportability was limited: in an independent non-SEER single-center cohort (\n                    <jats:italic>n<\/jats:italic>\n                    \u00a0=\u00a0150), discrimination was attenuated (cancer-specific AUC 0.59, 95% CI 0.49\u20130.69; overall 0.59, 0.50\u20130.68) and calibration drifted markedly [slope 0.47; observed-to-expected ratio (O:E) 1.23 and 1.19], with only the OM risk ordering preserved as a monotone gradient. This interpretable model therefore provides calibrated diagnostic-time risk stratification within SEER 2010\u20132020, intended not for treatment selection but for prognostic counseling, closer follow-up, and trial enrichment; local intercept-and-slope recalibration and larger multicenter validation are required before use outside that setting or window.\n                  <\/jats:p>","DOI":"10.3389\/fonc.2026.1904589","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:28:46Z","timestamp":1788150526000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable machine-learning risk stratification at the time of diagnosis for 3-year mortality in de novo metastatic prostate cancer: development and temporal validation in SEER"],"prefix":"10.3389","volume":"16","author":[{"given":"Xin","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Urology, The Second People\u2019s Hospital of Wuhu, Wuhu","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanglei","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Urology, The Second People\u2019s Hospital of Wuhu, Wuhu","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heqian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Urology, The Second People\u2019s Hospital of Wuhu, Wuhu","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Urology, The Second People\u2019s Hospital of Wuhu, Wuhu","place":["Anhui, 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