Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules.

ABSTRACT:

Background

Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients.

Methods

We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using ar

SUBMITTER: Kim RY 

PROVIDER: S-EPMC11521375 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature