Prostate Cancer Survival Prediction: Integrating Clinical Insights, Genomics and Machine Learning for Precision Care.

Prostate cancer remains a significant global health challenge among men, necessitating robust survival prediction models to enhance clinical decision-making and improve patient outcomes. While traditional prognostic tools—such as Gleason scores, PSA levels and TNM staging—offer valuable insights, they have limitations in precision, scalability and adaptability across diverse patient populations. This review integrates established predictors with advanced methodologies, including multiomics data integration, state-of-the-art imaging techniques and machine learning-driven analytics. A distinguishing aspect of this study is its focus on underexplored dimensions, such as psychosocial determinants, longitudinal modeling and real-world evidence (RWE), which collectively enhance model generalizability and practical applicability. Additionally, this review critically examines challenges related to ethical implementation, population-specific model validation, and equitable healthcare delivery. By proposing actionable frameworks for personalized medicine and inclusive strategies, this paper advances the discourse on survival prediction, paving the way for dynamic, patient-centered and ethically responsible approaches to prostate cancer management worldwide. [ABSTRACT FROM AUTHOR]