Liwen Zhang, Di Dong, Wenjuan Zhang, Mengjie Fang, Shuo Wang, Wuchao Li, Zaiyi Liu, Rongpin Wang, Junlin Zhou, Jie Tian
A deep learning (DL) model was proposed to predict the risk for OS based on computed tomography (CT) images. We retrospectively collected 640 patients from three independent centers, which were divided into a training cohort (center 1 and center 2, n=518) and an external validation cohort (center 3, n=122). We developed a DL model based on the architecture of residual convolutional neural network. The trained DL model significantly classified patients into high-risk and low-risk groups in training cohort (P-value<0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort (P-value<0.001, C-index:0.78, HR: 11.76). The DL model is a powerful model for risk assessment, and potentially serves as an individualized recommender for decision-making in GC patients.
The code of our deep leraning model is open access here and you can download.
If our code is helpful for your research, please cite the following paper:
Liwen Zhang, Di Dong, Wenjuan Zhang, Mengjie Fang, Shuo Wang, Wuchao Li, Zaiyi Liu, Rongpin Wang, Junlin Zhou, Jie Tian. A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study. (unpublished)