Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

• 作者: Arianna Rossi Chiara Dallaserra Federico Zamboni Gian Alberto Soardi Gino Puntel Giuseppe Sala Laura Spezia Manuel Signorini Massimiliano Motton Raffaele Augelli Simone Perandini Stefania Montemezzi
• 来源期刊: 世界放射学杂志：英文版（电子版）
• 学科: 医学
• 关键词: SOLITARY pulmonary NODULE COMPUTER-AIDED diagnosis Lung neoplasms MULTIDETECTOR COMPUTED tomography BAYESIAN prediction
• 年，卷（期）: 2016,（8）
• 页码范围: 729-734
• 页数: 6页
• 分类号: R563

摘要

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided 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.