Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories
Wěi Li, Shuye Lin, Yuqi He, Jinghui Wang, Yuanming Pan
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. Methods: = 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.
Topics & Concepts
MedicineColorectal cancerCohortOncologyInternal medicineCancerOverall survivalSurvival analysisArtificial intelligenceComputer scienceMachine Learning in HealthcareAI in cancer detectionRadiomics and Machine Learning in Medical Imaging