Predictive models of lymph node metastasis in patients with gastrointestinal stromal tumors based on machine learning algorithms: a SEER-based retrospective study.

ABSTRACT:

Background

Gastrointestinal stromal tumor (GIST) is one of the most prevalent tumors in the digestive system. Due to the rarity of lymph node metastasis in patients with GISTs, there is a scarcity of related studies, which leads to ongoing debates regarding its impact on patient prognosis. This study aimed to analyze the impact of lymph node metastasis on the overall survival of GISTs patients and further building predictive models with machine learning algorithms.

Methods

This is a retrospective study based on the Surveillance, Epidemiology and End Results (SEER) database. The demographic and clinicopathological characteristics of GISTs patients who underwent surgical therapy were collected from database. Five different types of machine learning algorithms were used to buil

SUBMITTER: Lin Z 

PROVIDER: S-EPMC11921308 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature