Deep learning-based lung cancer risk assessment using chest computed tomography images without pulmonary nodules ≥8 mm.

ABSTRACT:

Background

Low-dose chest computed tomography (LDCT) screening improves early detection of lung cancer but poses challenges such as false positives and overdiagnosis, especially for nodules smaller than 8 mm where follow-up guidelines are unclear. Traditional risk prediction models have limitations, and deep learning (DL) algorithms offer potential improvements but often require large datasets. This study aimed to develop a DL-based, label-free lung cancer risk prediction model using alternative LDCT images and validate it in individuals without non-calcified solid pulmonary nodules larger than 8 mm.

Methods

We utilized LDCT scans from individuals without non-calcified solid nodules larger than 8 mm to develop a DL-based lung cancer risk prediction model. An alternative trai

SUBMITTER: Yang S 

PROVIDER: S-EPMC11826273 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature