Colorectal cancer remains a major cause of cancer-related mortality worldwide, motivating the development of accurate prognostic models. Despite advances in machine learning for survival prediction, a common practice in the literature reformulates time-to-event outcomes as binary variables and applies standard classification algorithms, disregarding censoring and the temporal nature of survival data. In this study, we…
Clinical prediction models often underperform in deployment populations that differ from historical training cohorts because of covariate shifts, but outcome-based updating is infeasible when deployment outcomes are unavailable. This challenge is further amplified when the target training cohort has limited samples or sparse events. Here we propose CoxRTL, a transfer learning framework with deployment-covariate recalibration…
