Fusion of 2.5D deep transfer learning and radiomics for predicting benign and malignant Lung Imaging Reporting and Data System (Lung-RADS) 3 and 4A nodules.

ABSTRACT:

Background

Pulmonary nodules categorized as Lung Imaging Reporting and Data System (Lung-RADS) 3 and 4A constitute a substantial proportion of radiologically indeterminate lesions, and current imaging criteria demonstrate limited accuracy for distinguishing benign from malignant nodules. To develop and validate an integrated predictive model combining radiomics and two-and-a-half (2.5D) deep transfer learning (DTL) for differentiating benign from malignant pulmonary nodules classified as Lung-RADS categories 3 and 4A on computed tomography (CT).

Methods

This retrospective study included 298 patients with Lung-RADS 3 and 4A nodules from three centers. The cohort from Center 1 (n=247) was divided into training (n=172) and test (n=75) sets, while patients from Centers 2 and 3 (

SUBMITTER: Wang K 

PROVIDER: S-EPMC12603431 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature