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.

Authors

Xin Wang, Guanglei Yao, Heqian Liu, Wei Ding

Published in

Frontiers in oncology. Volume 16. Pages 1904589. Epub Aug 31, 2026.

Abstract

Men with de novo 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 (2010-2020), 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 ≥1 month) and split it temporally into training (2010-2016, n = 21,584) and validation (2017-2020, n = 19,473) 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) 0.693-0.708] for net CSM, a cause-specific quantity excluding other-cause deaths, not an absolute competing-risks cumulative incidence, and 0.704 (0.697-0.712) 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%-70% thresholds; discrimination was comparable across leading algorithms and to logistic regression. Temporal internal-external 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 2010-2020, 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 = 150), discrimination was attenuated (cancer-specific AUC 0.59, 95% CI 0.49-0.69; overall 0.59, 0.50-0.68) 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 2010-2020, 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.

PMID:
42741211
Bibliographic data and abstract were imported from PubMed on 15 Sep 2026.

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