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Proceedings Paper

Effect of dual-energy subtraction on performance of a commercial computer-assisted diagnosis system in detection of pulmonary nodules
Author(s): Eliot Siegel; Bruce Reiner; Khan Siddiqui; Amy Musk; Susan Wood; Xiaolan Zeng; Nabile Safdar; Paul G. Nagy; Frank Hooper; Ryan Moffitt; Steve Severance
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Paper Abstract

The relatively low (20%-25%) sensitivity of conventional radiography for lung nodules is an impetus for investigations into computer-assisted diagnostic (CAD) algorithms and into alternative acquisition techniques (such as dual-energy subtraction [DES]), both of which have been shown to increase diagnostic sensitivity for lung nodule detection. This pilot study combined these synergistic techniques in the diagnosis of digital clinical chest radiographs in 26 individuals. A total of 59 marks were identified by the CAD algorithm as suspicious for a nodule using a "conventional" chest direct radiography posterior/anterior image (an average of 2.3 marks per radiograph). Only 39 marks were identified on the soft tissue image of the corresponding DES radiographs (an average of 1.5 marks per radiograph). The sensitivity for nodules considered subtle but "actionable" in the 10-15-mm range was 0% (correctly identifying 0 of 4 nodules), whereas the sensitivity for the same radiographs with DES was 75% (correctly identifying 3 of 4 nodules). These pilot data suggest that the algorithms for at least one commercial CAD system may not be fully able to differentiate overlying bones and other calcifications from pulmonary lesions (which is also a difficult task for radiologists) and that the combination of CAD and DES acquisition may result in a substantial improvement in both sensitivity and specificity in the detection of relatively subtle lung nodules. This study has been expanded to evaluate a much larger set of images to further investigate the potential for the routine use of CAD with DES.

Paper Details

Date Published: 15 April 2005
PDF: 7 pages
Proc. SPIE 5748, Medical Imaging 2005: PACS and Imaging Informatics, (15 April 2005); doi: 10.1117/12.595932
Show Author Affiliations
Eliot Siegel, Baltimore VA Medical Ctr. (United States)
Univ. of Maryland School of Medicine (United States)
Bruce Reiner, Baltimore VA Medical Ctr. (United States)
Univ. of Maryland School of Medicine (United States)
Khan Siddiqui, Baltimore VA Medical Ctr. (United States)
Amy Musk, Baltimore VA Medical Ctr. (United States)
Univ. of Maryland School of Medicine (United States)
Susan Wood, Medicsight Inc. (United Kingdom)
Xiaolan Zeng, R2 Technology, Inc. (United States)
Nabile Safdar, Baltimore VA Medical Center (United States)
Univ. of Maryland School of Medicine (United States)
Paul G. Nagy, Univ. of Maryland School of Medicine (United States)
Frank Hooper, Baltimore VA Medical Ctr. (United States)
Univ. of Maryland School of Medicine (United States)
Ryan Moffitt, Baltimore VA Medical Ctr. (United States)
Steve Severance, Baltimore VA Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 5748:
Medical Imaging 2005: PACS and Imaging Informatics
Osman M. Ratib; Steven C. Horii, Editor(s)

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