Spectral dual-layer detector CT-based radiomics-deep learning for predicting pathological aggressiveness of stage I lung adenocarcinoma: discrimination of precursor glandular lesions and invasive adenocarcinomas.

ABSTRACT:

Background

Accurate diagnosis of early-stage lung adenocarcinoma (LA) subtypes is crucial for optimal patient management. Radiomics extract features from medical images reflect underlying biological information, while effective atomic number (Zeff) from new-generation spectral dual-layer detector computed tomography (SDCT) reflects tissue composition. This study evaluated the utility of SDCT-Zeff-based radiomics, deep learning (DL), and clinical features to differentiate between ground-glass nodule (GGN)-featured precursor glandular lesions (PGLs) and adenocarcinomas.

Methods

Patients diagnosed with GGN who underwent preoperative contrast-enhanced SDCT at two medical centers were prospectively enrolled between January 2022 and April 2024. Center 1 (Shengjing Hospital of Chin

SUBMITTER: Wang T 

PROVIDER: S-EPMC11921343 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature