Development and validation of a deep learning model for predicting postoperative survival of patients with gastric cancer.

ABSTRACT:

Background

Deep learning (DL), a specialized form of machine learning (ML), is valuable for forecasting survival in various diseases. Its clinical applicability in real-world patients with gastric cancer (GC) has yet to be extensively validated.

Methods

A combined cohort of 11,414 GC patients from the Surveillance, Epidemiology and End Results (SEER) database and 2,846 patients from a Chinese dataset were utilized. The internal validation of different algorithms, including DL model, traditional ML models, and American Joint Committee on Cancer (AJCC) stage model, was conducted by training and testing sets on the SEER database, followed by external validation on the Chinese dataset. The performance of the algorithms was assessed using the area under the receiver operating cha

SUBMITTER: Wu M 

PROVIDER: S-EPMC10916254 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature