Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis
Related Experiment Video
Updated: Mar 14, 2026
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
Published on: October 13, 2023
Simone Perandini1, Gian Alberto Soardi1, Massimiliano Motton1
1Simone Perandini, Gian Alberto Soardi, Massimiliano Motton, Raffaele Augelli, Chiara Dallaserra, Gino Puntel, Arianna Rossi, Giuseppe Sala, Manuel Signorini, Laura Spezia, Federico Zamboni, Stefania Montemezzi, Department of Radiology, Azienda Ospedaliera Universitaria Integrata di Verona, 37100 Verona, Italy.
Abstract:
The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.
