Interpretable machine learning model integrating CT radiomics, CTR, and clinical features for EGFR mutation prediction in ≤3 cm lung adenocarcinoma nodules.

Abstract excerpt

BACKGROUND: Non-invasive prediction of EGFR mutation status in lung adenocarcinoma (LUAD) is critical for treatment planning, particularly in small pulmonary nodules where tissue genotyping is limited. However, the consolidation-to-tumor ratio (CTR), a clinically relevant imaging biomarker, has rarely been incorporated into radiomics-based models. OBJECTIVE: To develop and validate an interpretable CT radiomics...

Topics

• Humans
• Female
• Lung Neoplasms
• ErbB Receptors
• Adenocarcinoma of Lung
• Machine Learning