| Title | Computer-aided Diagnosis for the Detection and Classification of Lung Cancers on Chest Radiographs ROC Analysis of Radiologists' Performance. | | Author(s) | Shiraishi J, Abe H, Li F, Engelmann R, Macmahon H, Doi K | | Institution | Department of Radiology, Kurt Rossmann Laboratories for Radiologic Image Research, The University of Chicago, 5841 South Maryland Avenue, MC2026 Chicago, IL 60637. | | Source | Acad Radiol 2006 Aug; 13(8):995-1003. | | Abstract | RATIONALE AND
OBJECTIVES: The aim of the study is to investigate the effect of a computer-aided diagnostic (CAD) scheme on radiologist performance in the detection of lung cancers on chest radiographs.
MATERIALS AND METHODS: We combined two independent CAD schemes for the detection and classification of lung nodules into one new CAD scheme by use of a database of 150 chest images, including 108 cases with solitary pulmonary nodules and 42 cases without nodules. For the observer study, we selected 48 chest images, including 24 lung cancers, 12 benign nodules, and 12 cases without nodules, from the database to investigate radiologist performance in the detection of lung cancers. Nine radiologists participated in a receiver operating characteristic (ROC) study in which cases were interpreted first without and then with computer output, which indicated locations of possible lung nodules, together with a five-color scale illustrating the computer-estimated likelihood of malignancy of the detected nodules.
RESULTS: Performance of the CAD scheme indicated that sensitivity in detecting lung nodules was 80.6%, with 1.2 false-positive results per image, and sensitivity and specificity for classification of nodules by use of the same database for training and testing the CAD scheme were 87.7% and 66.7%, respectively. Average area under the ROC curve value for detection of lung cancers improved significantly (P = .008) from without (0.724) to with CAD (0.778).
CONCLUSION: This type of CAD scheme, which includes two functions, namely detection and classification, can improve radiologist accuracy in the diagnosis of lung cancer. | | Language | eng | | Pub Type(s) | Journal Article
| | PubMed ID | 16843852 |
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