Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories

Abstract

Introduction Colorectal cancer (CRC) is the third most common cancer. Precise prediction of CRC patients’ overall survival (OS) probability could offer advice on its treatment. Neural network (NN) is the first-class algorithm, but a consensus on which NN survival models are better has not been established yet. A predictive model on CRC using Asian data is also lacking. Material and methods We conducted 8 NN survival models of CRC (n = 416) with different theories and compared them using Asian data. Results DeepSurv performed best with a C-index value of 0.8300 in the training cohort and 0.7681 in the test cohort. Conclusions The deep learning survival model for CRC patients (DeepCRC) could predict CRC’s OS accurately.

Keywords

• colorectal cancer
• neural network
• deep learning
• predictive model