A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study

2020 ◽  
Author(s):  
Liwen Zhang ◽  
Di Dong ◽  
Wenjuan Zhang ◽  
Xiaohan Hao ◽  
Mengjie Fang ◽  
...  
2020 ◽  
Vol 150 ◽  
pp. 73-80 ◽  
Author(s):  
Liwen Zhang ◽  
Di Dong ◽  
Wenjuan Zhang ◽  
Xiaohan Hao ◽  
Mengjie Fang ◽  
...  

2017 ◽  
Vol 128 (5) ◽  
pp. 1140-1145 ◽  
Author(s):  
Japke F. Petersen ◽  
Martijn M. Stuiver ◽  
Adriana J. Timmermans ◽  
Amy Chen ◽  
Hongzhen Zhang ◽  
...  

PLoS ONE ◽  
2016 ◽  
Vol 11 (5) ◽  
pp. e0156207 ◽  
Author(s):  
Jeung Hui Pyo ◽  
Hyuk Lee ◽  
Byung-Hoon Min ◽  
Jun Haeng Lee ◽  
Min Gew Choi ◽  
...  

2016 ◽  
Vol 19 (3) ◽  
pp. 97-101
Author(s):  
Hyo Jung Ko ◽  
Ki Hyun Kim ◽  
Si Hak Lee ◽  
Cheol Woong Choi ◽  
Su Jin Kim ◽  
...  

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 164-164 ◽  
Author(s):  
Woo Jin Hyung ◽  
Taeil Son ◽  
Minseok Park ◽  
Hansang Lee ◽  
Youn Nam Kim ◽  
...  

164 Background: Staging systems for cancer are critical to predict the prognosis of patients. Current staging systems for gastric cancer have limitations to predict individualized and precise prediction of patient’s survival after treatment. We aimed to develop prediction model based on deep learning by estimating the survival probability of patients who underwent gastrectomy. Methods: To predict the survival probability, we used a deep neural network model which consisted of 5 layers: input layer, 3 fully connected layer, and output layer with 8 characteristics (age, sex, histology, depth of tumor, number of metastatic and examined lymph node, presence of distant metastasis, and resection extent) of patients which was previously published Yonsei prediction model using Cox regression. Each layer functioned as the nonlinear weighted sum of lower layer. Five-year overall survival was predicted using the deep learning method and it was compared to Yonsei prediction model. The average area under the curve (AUC) was compared between the models. For internal validation, 5-fold cross validations were carried out. We also performed external validation with a dataset from another hospital (n = 1549). . Results: Deep learning predicted 5-year overall survival of patients with an average accuracy of 83.5% in the test set. The average AUC of deep learning by integrating 8 characteristics was significantly higher than that of Yonsei prediction model (AUC: 0.844 vs. 0.831, P < 0.001) with the same variables. In the external validation the average accuracy of survival prediction was 84.1%. The AUC was also greater in a dataset from other hospital in Korea (AUC: 0.852 vs. 0.847, P = 0.023) Conclusions: Prognosis prediction with deep learning showed superior survival predictive power compared to prediction model using Cox regression. It can provide individualized and precise stratification based on the risk using characteristics of gastric cancer patients.


2021 ◽  
Vol 83 (s5) ◽  
Author(s):  
Y. Yang ◽  
Z. Long ◽  
Zhengming Zhong ◽  
Qing Liu ◽  
X. Yang

2020 ◽  
Vol 67 ◽  
pp. 101766
Author(s):  
Hadrien Charvat ◽  
Taichi Shimazu ◽  
Manami Inoue ◽  
Motoki Iwasaki ◽  
Norie Sawada ◽  
...  

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