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

ABSTRACT:

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 model incorporating CTR and clinical features for predicting EGFR mutation status in LUAD patients with nodules ≤3 cm.

Methods

In this retrospective study included 492 patients with pathologically confirmed LUAD who underwent preoperative non-contrast chest CT between January 2017 and December 2022. Tumors were manually segmented for radiomic feature extraction,

SUBMITTER: Cai W 

PROVIDER: S-EPMC12720629 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature