Identifying Risk Stratification Associated with a Cancer for Overall Survival by Deep Learning-Based CoxPH

Abstract

In a time-to-event model, Cox proportional hazards (CoxPH) analysis is the most frequently used method for estimating overall survival. However, the CoxPH analysis is limited to explaining only single or partial risk effects among clinicopathological factors. We introduced DeepCoxPH, a risk score estimation strategy based on deep learning (DL) and CoxPH, to improve the risk stratification for overall survival analysis. The abstracted weight from the DL and the hazard ratios from the CoxPH were transformed into the risk score estimation in the fully adjusted model. The DeepCoxPH exhibited more comprehensive risk weight estimation for overall survival. The DeepCoxPH was applied to predict ten-year overall survival in breast cancer. A Kaplan-Meier curve revealed that the DeepCoxPH improved discrimination of high- A nd low-risk stratification in both short- A nd long-term breast cancer for overall survival. To the best of our knowledge, this is the first report of the risk score estimation based on machine learning and parametric-statistical analysis aimed at identifying risk stratification for overall survival through the consideration of comprehensive risk effects among multiple clinicopathological factors.