Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification.

ABSTRACT:

Background

Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most important predictors and unravel their interactions to classify patients, which might guide prioritized treatment decisions.

Methods

A total of 140 patients with histopathological confirmed GC who underwent surgery between 2011 and 2016 were enrolled in the study. The inspired modification of the partial least squares (SIMPLS)-based model was used to identify the most significant predictors and interactions between variables. Predictive partition analysis was employed to establish the decision tree model to prioritize markers for clin

SUBMITTER: Rafiepoor H 

PROVIDER: S-EPMC12042310 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature