Multimodal CT radiomics combined with machine learning algorithms to differentiate benign from malignant pulmonary nodules.

ABSTRACT:

Objectives

Although radiologists typically rely on imaging characteristics of pulmonary nodules for preoperative evaluation, the inherent subjectivity of this approach often leads to high misdiagnosis rates. This study comparatively analyzed the diagnostic value of non-contrast-enhanced computed tomography (NCECT) and contrast-enhanced computed tomography (CECT) in differentiating benign and malignant pulmonary nodules using multi-regional radiomics and machine learning algorithms.

Methods

This retrospective collection included 194 patients who underwent NCECT and CECT scans. Radiomics features were extracted by identifying the intra-nodular and peri-nodular 5 mm area as the region of interest. Six different machine learning classifiers were used to select the most effective

SUBMITTER: Liu L 

PROVIDER: S-EPMC12852588 | biostudies-literature | 2026 Jan-Dec

REPOSITORIES: biostudies-literature