Paper
22 May 2003 Identification of missed pulmonary nodules on low-dose CT lung cancer screening studies using an automatic detection system
Carol L. Novak, Li Fan, Jianzhong Qian, Guo-Qing Wei, David P. Naidich M.D.
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Abstract
Multi-slice CT (MSCT) scanners allow nodules as small as 3mm to be identified during screening. However the associated large data sets make it challenging for radiologists to identify all small nodules in a reasonable amount of time. Computer-aided detection may play a critical role in identifying missed nodules. 13 MSCT screening studies, initially interpreted as "non-actionable" by a radiologist, were selected from participants in a lung cancer screening study. The study protocol defines "actionable" studies as those containing at least 1 solid non-calcified nodule larger than 3mm, for which follow-up studies are recommended to exclude interval growth. An automatic detection algorithm was applied to the 13 studies to determine whether it might detect missed nodules, and whether any of these were of sufficient size to be considered "actionable". There were a total of 138 automatically detected candidate nodules, an average of 10.6 per patient. 83 candidates were characterized as true positives, yielding a positive predictive value of 60.1%. 10 automatically detected candidates were judged to be actionable nodules greater than 3mm in diameter. 6 of 13 (46%) patients had at least one "actionable" finding detected by the computer that had been overlooked in the initial exam.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Carol L. Novak, Li Fan, Jianzhong Qian, Guo-Qing Wei, and David P. Naidich M.D. "Identification of missed pulmonary nodules on low-dose CT lung cancer screening studies using an automatic detection system", Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); https://doi.org/10.1117/12.480101
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Cited by 7 scholarly publications.
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KEYWORDS
Lung cancer

Feature extraction

Solids

Computed tomography

Detection and tracking algorithms

Lung

Computer aided diagnosis and therapy

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