Abstract:
: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.
journal_name
Radiologyjournal_title
Radiologyauthors
Kim KG,Goo JM,Kim JH,Lee HJ,Min BG,Bae KT,Im JGdoi
10.1148/radiol.2372041461subject
Has Abstractpub_date
2005-11-01 00:00:00pages
657-61issue
2eissn
0033-8419issn
1527-1315pii
2372041461journal_volume
237pub_type
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