Authors:Ruwen Zhou^1,#, Zhijie Wu^2,#, Tingyang Xu^3,#, Jian Cai⁴, Shengyu Wang⁵, Yang Li², Yebiao Zhao⁶, Wenle Chen⁷, Duo Liu⁴, Hui Wang^2,*, Jing Lu^2,*, Zixu Yuan^2,*

1. Lee Kong Chian School of Medicine, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798.
2. Department of Colorectal and Anal Surgery, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China.
3. Tencent AI Lab, Shenzhen, China.
4. Department of Colorectal Surgery, Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen University School of Medicine, Shenzhen, China.
5. Department of Medical Engineering, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China
6. Department of General Surgery, Dongguan Houjie Hospital, Dongguan, China.
7. Department of Colorectal Surgery, Zhongshan Municipal Hospital, Zhongshan, China.
# Ruwen Zhou, Zhijie Wu and Tingyang Xu are co-first authors.
*These authors contributed equally to this work.

Abstract:

Background :
Peritoneal metastasis (PM) has been considered to be the terminal stage of colorectal cancer (CRC) due to poor prognosis. We purposed to construct an AI model of clinicopathological parameters to predict the survival prognosis of PM in CRC.
Methods :
Our model was constructed through modifying a classic neural network with COX proportional hazards as loss function in the training cohort. It is able to predict the overall survival (OS) of each individual patient with clinicopathological parameters. Multivariate analysis was conducted to identify independent risk factors for the prognosis of PM patients.
Results and Conclusion :
In the testing cohort, the deep learning model show good performance with the c-index of 0.76 and brier score of 0.20.
Conclusions :
We have developed a deep learning model to predict the survival of individual patients precisely. It can provide evidence to apply personalized treatments and assisted surgeon to select optical treatments for CRC patients with PM.

Keywords:
Artificial Intelligent, Deep Learning, Colorectal Cancer, Peritoneal Metastasis, Survival.

Citation:

Zixu Yuan. Development of a deep learning survival model to predict the individual risks of peritoneal metastasis of colorectal cancer. Japanese Journal of Gastroenterology 2022.