NSCLC EGFR Mutation Prediction via Random Forest Model: A Clinical-CT-Radiomics Integration Approach.

ABSTRACT: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Accurate determination of epidermal growth factor receptor (EGFR) mutation status is essential for selecting patients eligible for tyrosine kinase inhibitors (TKIs). However, invasive genotyping is often limited by tissue accessibility and sample quality. This study presents a non-invasive machine learning model combining clinical data, CT morphological features, and radiomic descriptors to predict EGFR mutation status. A retrospective cohort of 138 patients with confirmed EGFR status and pre-treatment CT scans was analyzed. Radiomic features were extracted with PyRadiomics, and feature selection applied mutual information, Spearman correlation, and wrapper-based methods. Five Random Forest model

SUBMITTER: Benfares A 

PROVIDER: S-EPMC12562246 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature