Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress.

ABSTRACT:

Background

Oxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies investigating the relationship between oxidative stress and prognosis of elderly patients with esophageal squamous cancer (ESCC).

Methods

This study included elderly patients with ESCC who underwent curative ESCC resection surgery continuously from January 2013 to December 2020 and were stratified into the training and external validation cohorts. Using Cox stepwise regression analysis based on Akaike information criterion, the relationship between oxidative stress biomarkers and prognosis was explored, and a geriatric ESCC-related oxidative stress score (OSS) was constructed. To construct a predictive model for 3-year overall survi

SUBMITTER: Xie JB 

PROVIDER: S-EPMC11580478 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature