Toward Precision Medicine: Development and Validation of A Machine Learning Based Decision Support System for Optimal Sequencing in Castration-Resistant Prostate Cancer.

INTRODUCTION

Selecting a patient-specific sequencing strategy to maximize survival outcomes is a clinically unmet need for patients with castration-resistant prostate cancer (CRPC). We developed and validated an artificial intelligence-based decision support system (DSS) to guide optimal sequencing strategy selection.

PATIENTS AND METHODS

Clinicopathological data of 46 covariates were retrospectively collected from 801 patients diagnosed with CRPC at 2 high-volume institutions between February 2004 and March 2021. Cox-proportional hazards regression survival (Cox) modeling in extreme gradient boosting (XGB) was used to perform survival analysis for cancer-specific mortality (CSM) and overall mortality (OM) according to the use of abiraterone acetate, cabazitaxel, docetaxel, and enzalutamide. The models were further stratified into first-, second-, and third-line models that each provided CSM and OM estimates for each line of treatment. The performances of the XGB models were compared with those of the Cox models and random survival forest (RSF) models in terms of Harrell's C-index.

RESULTS

The XGB models showed greater predictive performance for CSM and OM compared to the RSF and Cox models. C-indices of 0.827, 0.807, and 0.748 were achieved for CSM in the first-, second-, and third-lines of treatment, respectively, while C-indices of 0.822, 0.813, and 0.729 were achieved for OM regarding each line of treatment, respectively. An online DSS was developed to provide visualization of individualized survival outcomes according to each line of sequencing strategy.

CONCLUSION

Our DSS can be used in clinical practice by physicians and patients as a visualized tool to guide the sequencing strategy of CRPC agents.