Machine learning-based model for predicting distant lymph node metastasis in esophageal cancer.

ABSTRACT:

Background

Distant lymph node metastasis (LNM) from esophageal cancer is relatively difficult to detect and is associated with a poor prognosis. Our study aimed to develop machine learning models for predicting distant LNM in esophageal cancer, with the goal of enhancing diagnostic accuracy, optimizing treatment strategies, and identifying high-risk patients for personalized clinical management.

Methods

The demographic and clinicopathological data of patients diagnosed with esophageal cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database, 17 Registries, November 2023 (2000-2021). Univariate and multivariate logistic regression analyses were applied to select correlation variances related to distant LNM. Seven machine learning models were

SUBMITTER: Wang G 

PROVIDER: S-EPMC12557644 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature