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Computer-aided Diagnosis of Localized Ground-Glass Opacity in the Lung at CT: Initial Experience¹

¹ From the Department of Radiology, Seoul National University College of Medicine, and the Institute of Radiation Medicine, SNUMRC, 28 Yongon-dong, Chongno-gu, Seoul 110-744, Korea (K.G.K., J.M.G., J.H.K., H.J.L., J.G.I.); Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea (B.G.M.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (K.T.B.). Received August 23, 2004; revision requested October 28; revision received November 19; accepted December 24. Supported in part by Seoul National University Hospital Research Grant (0420030300) and by a grant from the Korea Health 21 Research and Development Project-Ministry of Health and Welfare, Republic of Korea (03-PJ1-PG10-51300-0006). Address correspondence to J.M.G. (e-mail: jmgoo{at}plaza.snu.ac.kr).

The purpose of this study was to develop an automated scheme to facilitate detection of localized ground-glass opacity (GGO) in the lung at computed tomography (CT). Institutional review board approval and informed consent were not required. Two radiologists reviewed CT images from 14 patients (five men, nine women) who had lung cancer or metastasis and whose malignancy was classified as GGO. The lung region was sampled and completely covered with contiguous, 50% overlapping regions of interest (ROIs) measuring 30 x 30 pixels in size. The lung area within each ROI was analyzed to compute texture features and gaussian curve fitting features. Performance of the artificial neural networks (ANNs) measured by using the area under the receiver operating characteristic curve was 0.92. With a threshold of 0.9, the sensitivity of the ANN for detecting GGO ROIs was 94.3% (280 of 297 ROIs), and the positive predictive value was 29.1% (280 of 963 ROIs). A computerized scheme may hold promise in facilitating detection of localized GGO at CT.

© RSNA, 2005

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