Computer-Aided Diagnosis of Lung Nodules on CT Scans

• Published in: Academic radiology Vol. 17; no. 3; pp. 323 - 332
• Main Authors: Way, Ted, PhD, Chan, Heang-Ping, PhD, Hadjiiski, Lubomir, PhD, Sahiner, Berkman, PhD, Chughtai, Aamer, MD, Song, Thomas K., MD, Poopat, Chad, MD, Stojanovska, Jadranka, MD, Frank, Luba, MD, Attili, Anil, MD, Bogot, Naama, MD, Cascade, Philip N., MD, Kazerooni, Ella A., MD
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-03-2010
• Subjects: computed tomography; Computer-aided diagnosis; observer study; pulmonary nodule; Radiology; computed tomography; observer study; pulmonary nodule; Computer-aided diagnosis
• ISSN: 1076-6332; 1878-4046
• DOI: 10.1016/j.acra.2009.10.016

Rationale and Objectives The aim of this study was to evaluate the effect of computer-aided diagnosis (CAD) on radiologists' estimates of the likelihood of malignancy of lung nodules on computed tomographic (CT) imaging. Methods and Materials A total of 256 lung nodules (124 malignant, 132 benign) were retrospectively collected from the thoracic CT scans of 152 patients. An automated CAD system was developed to characterize and provide malignancy ratings for lung nodules on CT volumetric images. An observer study was conducted using receiver-operating characteristic analysis to evaluate the effect of CAD on radiologists' characterization of lung nodules. Six fellowship-trained thoracic radiologists served as readers. The readers rated the likelihood of malignancy on a scale of 0% to 100% and recommended appropriate action first without CAD and then with CAD. The observer ratings were analyzed using the Dorfman-Berbaum-Metz multireader, multicase method. Results The CAD system achieved a test area under the receiver-operating characteristic curve ( Az ) of 0.857 ± 0.023 using the perimeter, two nodule radii measures, two texture features, and two gradient field features. All six radiologists obtained improved performance with CAD. The average Az of the radiologists improved significantly ( P < .01) from 0.833 (range, 0.817–0.847) to 0.853 (range, 0.834–0.887). Conclusion CAD has the potential to increase radiologists' accuracy in assessing the likelihood of malignancy of lung nodules on CT imaging.