TITLE:
AI-Driven Insights Identify PSA, Frailty, and Comorbidities as Key Survival Predictors in mCRPC
AUTHORS:
Bahareh Rahmani, Pavan Sai Gajula, Mustaid Ahmed, Naga Pushpa Latha Ginni, Vanitha Ajmeer, Jason Doherty, Sailaja Vatsalya Madhurapantula, Payam Norouzzadeh, Leslie Hinyard, Mostafa Yazdi, Martin Schoen
KEYWORDS:
Metastatic Castration, Resistant Prostate Cancer, Prostate-Specific Antigen, Comorbidity Scores, Support Vector Machine, Logistic Regression, Random Forest
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.14 No.9,
September
17,
2026
ABSTRACT: Metastatic Castration-Resistant Prostate Cancer (mCRPC) presents significant therapeutic challenges and requires data-driven strategies to enhance patient outcomes. This study compared and analyzed the efficacy of two second-generation androgen receptor inhibitors, enzalutamide and abiraterone, and identified the key predictors of mortality. Models such as logistic regression, Random Forest, and support vector machine (SVM) were evaluated using patient-level data, with Random Forest achieving the highest accuracy (96.7%) and demonstrating strong generalizability across cross-validation folds (mean score: 0.972). Comorbidity scores, Prostate-Specific Antigen (PSA) levels, and frailty indices were consistently found to be the best indicators of death, highlighting the importance of systemic health over treatment choice in determining survival. While logistic regression provided interpretability and SVM showed competitive accuracy, Random Forest proved to be the most effective for risk stratification and personalized treatment planning. These findings demonstrate the potential of machine learning models for mortality risk stratification in mCRPC and highlight the importance of incorporating systemic health indicators into predictive oncology models.