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Thoracic Imaging |
1 From the Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637 (S.G.A., F.L., M.L.G., H.M., K.D.); and Department of Radiology, Azumi General Hospital, Nagano, Japan (S.S.). From the 2001 RSNA scientific assembly. Received August 13, 2001; revision requested October 10; revision received March 8, 2002; accepted May 7. Supported in part by United States Public Health Service grants CA83908, CA64370, and CA62525, funding from the University of Chicago Cancer Research Center, and a grant from the Grant Healthcare Foundation through the Medical Imaging and Research Foundation of the University of Chicago. Address correspondence to S.G.A. (e-mail: s-armato@uchicago.edu).
PURPOSE: To evaluate the performance of a fully automated computerized method for the detection of lung nodules in computed tomographic (CT) scans in the identification of lung cancers that may be missed during visual interpretation.
MATERIALS AND METHODS: A database of 38 low-dose CT scans with 50 lung nodules was obtained from a lung cancer screening program. Thirty-eight of the nodules represented biopsy-confirmed lung cancers that had not been reported during initial clinical interpretation. A computer detection method that involved the use of gray-level thresholding techniques to identify three-dimensionally contiguous structures within the lungs was applied to the CT data. Computer-extracted volume was used to determine whether a structure became a nodule candidate. A rule-based scheme and a cascaded automated classifier were applied to the set of nodule candidates to distinguish actual nodules from areas of normal anatomy. Overall performance of the computer detection method was evaluated with free-response receiver operating characteristic (FROC) analysis.
RESULTS: At a specific operating point on the FROC curve, the method achieved a sensitivity of 80% (40 of 50 nodules), with an average of 1.0 false-positive detection per section. Missed cancers were detected by the computerized method with a sensitivity of 84% (32 of 38 nodules) and a false-positive rate of 1.0 per section.
CONCLUSION: With an automated lung nodule detection method, a large fraction (84%, 32 of 38) of missed cancers in a database of low-dose CT scans were detected correctly.
© RSNA, 2002
Index terms: Cancer screening • Computed tomography (CT), image processing • Computers, diagnostic aid • Lung neoplasms, CT, 60.12115, 60.30
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