Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience.
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.
Kim
Kwang Gi
KG
Department of Radiology, Seoul National University College of Medicine, Chongno-gu, Seoul 110-744, Korea.
Goo
Jin Mo
JM
Kim
Jong Hyo
JH
Lee
Hyun Ju
HJ
Min
Byung Goo
BG
Bae
Kyongtae T
KT
Im
Jung-Gi
JG
D000368:Aged; D000369:Aged, 80 and over; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D014057:Tomography, X-Ray Computed