Personalized diagnosis of radiation pneumonitis in breast cancer patients based on radiomics.

ABSTRACT:

Objective

This study aimed to identify CT-based radiomic alterations associated with radiation pneumonitis (RP) and to evaluate the feasibility of machine learning classifiers for personalized RP diagnosis in breast cancer patients using these radiomic signatures.

Methods

A total of 146 planning CT scans (pre- and post-radiotherapy) from 73 breast cancer patients with confirmed RP were retrospectively analyzed. The entire lung was delineated as the region of interest (ROI), and 1,834 radiomic features were extracted using PyRadiomics. Feature selection was performed sequentially using the Mann-Whitney U-test (p < 0.05), Spearman's rank correlation (|ρ| < 0.9), and least absolute shrinkage and selection operator (LASSO). Eight classifiers [logistic regression (LR), support ve

SUBMITTER: Wen X 

PROVIDER: S-EPMC12324165 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature