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

被引：10
|
作者：
Li, Wei [1 ]
Lin, Shuye [1 ]
He, Yuqi [2 ]
Wang, Jinghui [1 ,3 ]
Pan, Yuanming [1 ]
机构：
[1] Capital Med Univ, Beijing Chest Hosp, Canc Res Ctr, Beijing TB & Thorac Tumor Res Inst, Beijing 101149, Peoples R China
[2] Capital Med Univ, Beijing Chest Hosp, Dept Gastroenterol, Beijing, Peoples R China
[3] Capital Med Univ, Beijing Chest Hosp, Dept Oncol, Beijing, Peoples R China
来源：
ARCHIVES OF MEDICAL SCIENCE | 2023年 / 19卷 / 01期
关键词：
colorectal cancer; neural network; deep learning; predictive model; TNM; CARCINOMA;
D O I：
10.5114/aoms/156477
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
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: 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.
引用
收藏
页码：264 / 269
页数：6
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