Paper
12 April 2002 Interobserver variations on interpretation of multislice CT lung cancer screening studies, and the implications for computer-aided diagnosis
Carol L. Novak, JianZhong Qian, Li Fan, Jane P. Ko, Ami N. Rubinowitz, Georgeann McGuinness, David Naidich M.D.
Author Affiliations +
Abstract
With low dose multi-slice CT for screening of lung cancer, physicians are now finding and examining increasingly smaller nodules. However as the size of detectable nodules becomes smaller, there may be greater differences among physicians as to what is detected and what constitutes a nodule. In this study, 10 CT screening studies of smokers were individually evaluated by three thoracic radiologists. After consensus to determine a gold standard, the number of nodules detected by individual radiologists ranged from 1.4 to 2.1 detections per patient. Each radiologist detected nodules missed by the other two. Although a total of 26 true nodules were detected by one or more radiologists, only 8 (31%) were detected by all three radiologists. The number of true nodules detected by an integrated automatic detection algorithm was 3.2 per patient after radiologist validation. Including these nodules in the gold standard set reduced the sensitivity of nodule detection by each radiologist to less than half. The sensitivity of nodule detection by the computer was better at 64%, proving especially efficacious for detecting smaller and more central nodules. Use of the automatic detection module would allow individual radiologists to increase the number of detected nodules by 114% to 207%.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Carol L. Novak, JianZhong Qian, Li Fan, Jane P. Ko, Ami N. Rubinowitz, Georgeann McGuinness, and David Naidich M.D. "Interobserver variations on interpretation of multislice CT lung cancer screening studies, and the implications for computer-aided diagnosis", Proc. SPIE 4686, Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment, (12 April 2002); https://doi.org/10.1117/12.462663
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Cited by 17 scholarly publications.
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KEYWORDS
Lung cancer

Computer aided diagnosis and therapy

Gold

Detection and tracking algorithms

Lung

Computing systems

Visualization

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