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 ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Prediction Model ◽  
Risk Prediction ◽  
Multicenter Study ◽  
Risk Prediction Model

A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study

2020 ◽  
Vol 150 ◽  
pp. 73-80 ◽  
Author(s):  
Liwen Zhang ◽  
Di Dong ◽  
Wenjuan Zhang ◽  
Xiaohan Hao ◽  
Mengjie Fang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Prediction Model ◽  
Risk Prediction ◽  
Multicenter Study ◽  
Risk Prediction Model

scholarly journals Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

2017 ◽  
Vol 128 (5) ◽  
pp. 1140-1145 ◽  
Author(s):  
Japke F. Petersen ◽  
Martijn M. Stuiver ◽  
Adriana J. Timmermans ◽  
Amy Chen ◽  
Hongzhen Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prediction Model ◽  
Risk Prediction ◽  
External Validation ◽  
Risk Prediction Model ◽  
Larynx Cancer

scholarly journals A Risk Prediction Model Based on Lymph-Node Metastasis in Poorly Differentiated–Type Intramucosal Gastric Cancer

PLoS ONE ◽  
2016 ◽  
Vol 11 (5) ◽  
pp. e0156207 ◽  
Author(s):  
Jeung Hui Pyo ◽  
Hyuk Lee ◽  
Byung-Hoon Min ◽  
Jun Haeng Lee ◽  
Min Gew Choi ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Node Metastasis ◽  
Model Based ◽  
Poorly Differentiated

scholarly journals Evaluation of P-POSSUM as a Risk Prediction Model in Laparoscopic Gastrectomy of Elderly Patients with Gastric Cancer

2016 ◽  
Vol 19 (3) ◽  
pp. 97-101
Author(s):  
Hyo Jung Ko ◽  
Ki Hyun Kim ◽  
Si Hak Lee ◽  
Cheol Woong Choi ◽  
Su Jin Kim ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Elderly Patients ◽  
Prediction Model ◽  
Risk Prediction ◽  
Laparoscopic Gastrectomy ◽  
Risk Prediction Model

Superior prognosis prediction performance of deep learning for gastric cancer compared to Yonsei prognosis prediction model using Cox regression.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 164-164 ◽  
Author(s):  
Woo Jin Hyung ◽  
Taeil Son ◽  
Minseok Park ◽  
Hansang Lee ◽  
Youn Nam Kim ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Prediction Model ◽  
Survival Probability ◽  
Cox Regression ◽  
External Validation ◽  
Prognosis Prediction ◽  
Average Accuracy ◽  
Staging Systems

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.

scholarly journals Construction and Evaluation of Gastric Cancer Risk Prediction Model

2021 ◽  
Vol 83 (s5) ◽  
Author(s):  
Y. Yang ◽  
Z. Long ◽  
Zhengming Zhong ◽  
Qing Liu ◽  
X. Yang
Keyword(s):  
Gastric Cancer ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Gastric Cancer Risk

Estimation of the performance of a risk prediction model for gastric cancer occurrence in Japan: Evidence from a small external population

2020 ◽  
Vol 67 ◽  
pp. 101766
Author(s):  
Hadrien Charvat ◽  
Taichi Shimazu ◽  
Manami Inoue ◽  
Motoki Iwasaki ◽  
Norie Sawada ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Cancer Occurrence ◽  
External Population
Sign in / Sign up

Export Citation Format

Share Document