Comprehensive Summary
In this study, Rajih et al discuss the outcomes of robotic-assisted radical prostatectomy (RARP) for the treatment of prostate cancer. Current advancements are still lacking in the ability to appropriately predict post-operative outcomes. This group illustrates how integration of machine learning (ML) may lead to the identification of complex non-linear relationships and interactions that classic statistical methods struggle to visualize. Utilizing The Random Forest model on 758 patients undergoing RARP, ML was found to have superior discriminative ability. This study furthermore provides a framework for the development of similar ML models to be employed across institutions to promote an understanding of the generalizability of this method.
Outcomes and Implications
Utilizing ML in procedures such as RARP enable providers to capture personalized probability estimates and avoid categorical risk classifications to optimize patient care and follow-up. Furthermore, this technology allows for improved patient counseling by providing individualized risk estimates to assist health care professionals to facilitate collaborative care efforts with realistic bounds. From a research perspective, this tool can be used to enhance studies via ML-derived risk scores that may allow clinical trials to be designed effectively and efficiently with a better-defined patient population.