Machine learning models based on log odds of positive lymph nodes for predicting survival in T₁N₊ gastric cancer.

ABSTRACT:

Background

Although early gastric cancer (EGC) is generally limited to the mucosal and submucosal layers, lymph node metastasis can still occur, which may worsen the prognosis, particularly when the number of examined lymph nodes (ELNs) is inadequate. This study introduces log odds of positive lymph nodes (LODDS) as a prognostic factor and integrates it with machine learning to improve survival predictions in T₁N₊ gastric cancer (GC).

Methods

This retrospective study used data from the Surveillance, Epidemiology, and End Results (SEER) Program and an independent validation cohort from the Chinese People's Liberation Army General Hospital First Medical Center. Predictive factors were selected using LASSO regression and multivariate Cox regression. Cox pr

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12827109 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature