| 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 þ LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses. Results: Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P 1⁄4 .012). The AUCs for the Mayo and Mayo þ LCP models were 0.58 (95% CI 1⁄4 0.48 to 0.69) and 0.65 (95% CI 1⁄4 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo þ LCP model was associated with increased sensitivity (56% vs 38%; P 1⁄4 .019), similar false positive rate (33% vs 35%; P 1⁄4 .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model. Conclusions: Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification. © The Author(s) 2024. Published by Oxford University Press. |
