ArticleFrontiers in oncology2026
Development and validation of an online predictive model for biochemical recurrence after radical prostatectomy in elderly patients.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
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7 authors.
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Abstract
Objective: To develop and validate a novel model for predicting biochemical recurrence (BCR) in elderly prostate cancer (PCa) patients after radical prostatectomy (RP) and to create an accessible online tool for its clinical application. Methods: This retrospective study included patients who underwent RP at two independent medical centers. The initial cohort included 450 patients (2015-2022), which were randomly divided into a training set (n = 315) and an internal validation set (n = 135) at a 7:3 ratio. An independent cohort of 175 patients (2013-2023) was used as the external validation set. Potential predictors were screened via univariable Cox regression. The independent prognostic factors for BCR were subsequently identified via multivariate Cox regression. A predictive nomogram was developed on the basis of these independent factors. The model performance was assessed via time-dependent ROC curves, calibration curves, decision curve analysis (DCA), and Kaplan-Meier (KM) curves. Results: Cox multivariate regression analysis revealed that Gleason score (GS), lymph node metastasis (LNM), seminal vesicle invasion (SVI), and free prostate-specific antigen (fPSA) were independent risk factors for BCR after RP in the elderly population (all Conclusion: We developed a validated nomogram based on four independent risk factors-the Gleason score, lymph node metastasis, seminal vesicle invasion, and free PSA-for predicting BCR in elderly prostate cancer patients after radical prostatectomy. This model demonstrated robust predictive performance across multiple validation sets. The accompanying web-based tool facilitates rapid and individualized risk assessment, aiding in clinical decision-making.
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