Predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer with two step feature selection and ensemble learning.

ABSTRACT: Patients with locally advanced rectal cancer (LARC) show substantial individual variability and a pronounced imbalance in response distribution to neoadjuvant chemoradiotherapy (nCRT), posing significant challenges to treatment response prediction. This study aims to identify effective predictive biomarkers and develop an ensemble learning-based prediction model to assess the response of LARC patients to nCRT. A two-step feature selection method was developed to identify predictive biomarkers by deriving stable reversal gene pairs through within-sample relative expression orderings (REOs) from LARC patients undergoing nCRT. Preliminary screening utilized four methods-MDFS, Boruta, MCFS, and VSOLassoBag-to form a candidate feature set. Secondary screening ranked these features by permutatio

SUBMITTER: Qian C 

PROVIDER: S-EPMC11929819 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature