Predicting radiation pneumonitis with dose-segmented radiomics in locally advanced non- small cell lung cancer patients undergoing consolidative immunotherapy post- concurrent chemoradiotherapy.

ABSTRACT:

Objective

To develop and validate a machine learning model that integrates dose distribution-based radiomics, clinicopathological parameters, and hematological inflammatory biomarkers for predicting radiation pneumonitis (RP) in locally advanced non-small cell lung cancer (LA-NSCLC) patients receiving immuno-consolidation therapy after concurrent chemoradiation (CCRT).

Methods

This retrospective study analyzed 161 locally advanced non-small cell lung cancer (LA-NSCLC) patients divided into training (n=112) and validation (n=49) cohorts. Radiomics features were extracted from planning CT scans across nine 5-Gy dose gradients (0-60 Gy), including the initial positioning CT (before radiotherapy) and a resetting CT (after a cumulative dose of 40-50 Gy), all within regions of int

SUBMITTER: Wang W 

PROVIDER: S-EPMC12510859 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature