Development and Validation of Deep Learning Model for Intermediate-Stage Hepatocellular Carcinoma Survival with Transarterial Chemoembolization (MC-hccAI 002): a Retrospective, Multicenter, Cohort Study.

ABSTRACT: Background: There are few effective prediction models for intermediate-stage hepatocellular carcinoma (IM-HCC) patients treated with transarterial chemoembolization (TACE) to predict overall survival (OS) is available. The learning survival neural network (DeepSurv) was developed to showed a better performance than cox proportional hazards model in prediction of OS. This study aimed to develop a deep learning-based prediction model to predict individual OS. Methods: This multicenter, retrospective, cohort study examined data from the electronic medical record system of four hospitals in China between January 1, 2007, to December 31, 2016. Patients were divided into a training set(n=1075) and a test set(n=269) at a ratio of 8:2 to develop a deep learning-based algorithm (deepH

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10905396 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature