基于亚洲临床数据的结直肠癌患者深度学习生存模型（DeepCRC）与不同理论的比较。
Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories.
作者信息
Li Wei, Lin Shuye, He Yuqi, Wang Jinghui, Pan Yuanming
机构信息
Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Tongzhou District, Beijing, China.
Department of Gastroenterology, Beijing Chest Hospital, Capital Medical University, Tongzhou District, Beijing, China.
出版信息
Arch Med Sci. 2023 Jan 13;19(1):264-269. doi: 10.5114/aoms/156477. eCollection 2023.
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 ( = 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.
引言
结直肠癌（CRC）是第三大常见癌症。准确预测CRC患者的总生存（OS）概率可为其治疗提供建议。神经网络（NN）是一流的算法，但尚未就哪种NN生存模型更好达成共识。目前也缺乏使用亚洲数据的CRC预测模型。
方法
我们使用亚洲数据构建了8种基于不同理论的CRC患者NN生存模型（n = 416）并进行比较。
结果
DeepSurv表现最佳，在训练队列中的C指数值为0.8300，在测试队列中为0.7681。
结论
CRC患者的深度学习生存模型（DeepCRC）可准确预测CRC的OS。
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