Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.

ABSTRACT:

Background

Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results.

Methods

A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the co

SUBMITTER: Li Y 

PROVIDER: S-EPMC10883857 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature