Diagnostic Advances
Copyright ©The Author(s) 2016. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Aug 28, 2016; 8(8): 729-734
Published online Aug 28, 2016. doi: 10.4329/wjr.v8.i8.729
Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis
Simone Perandini, Gian Alberto Soardi, Massimiliano Motton, Raffaele Augelli, Chiara Dallaserra, Gino Puntel, Arianna Rossi, Giuseppe Sala, Manuel Signorini, Laura Spezia, Federico Zamboni, Stefania Montemezzi
Simone 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
Author contributions: Perandini S, Soardi GA, Motton M and Montemezzi S designed and wrote the manuscript; Augelli R, Dallaserra C, Puntel G, Rossi A, Sala G, Signorini M, Spezia L and Zamboni F equally collected and analyzed the data in addition to performing literature search and manuscript revision.
Conflict-of-interest statement: The authors declare no conflicts of interest regarding this manuscript.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Simone Perandini, MD, Department of Radiology, Azienda Ospedaliera Universitaria Integrata di Verona, Piazzale Stefani 1, 37100 Verona, Italy. simone.perandini@ospedaleuniverona.it
Telephone: +39-045-8122124 Fax: +39-045-8122124
Received: February 18, 2016
Peer-review started: February 21, 2016
First decision: March 25, 2016
Revised: April 12, 2016
Accepted: July 11, 2016
Article in press: July 13, 2016
Published online: August 28, 2016
Processing time: 190 Days and 6.7 Hours
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.

Keywords: Solitary pulmonary nodule, Computer-aided diagnosis, Lung neoplasms, Multidetector computed tomography, Bayesian prediction

Core tip: The aim of this study was to prospectively assess the net accuracy gain of using computer-aided diagnosis in characterizing solitary pulmonary nodules detected at computed tomography. One-hundred randomly selected nodules with a definitive diagnosis were reviewed by 7 radiologists, before and after computer predictions. A net gain in diagnostic accuracy was found in 6 out of 7 readers. This study provides further evidence supporting the integration of computer aided diagnosis in nodule characterization.