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Proceedings Paper

Fully automated gynecomastia quantification from low-dose chest CT
Author(s): Shuang Liu; Emily B. Sonnenblick; Lea Azour; David F. Yankelevitz; Claudia I. Henschke; Anthony P. Reeves
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Paper Abstract

Gynecomastia is characterized by the enlargement of male breasts, which is a common and sometimes distressing condition found in over half of adult men over the age of 44. Although the majority of gynecomastia is physiologic or idiopathic, its occurrence may also associate with an extensive variety of underlying systemic disease or drug toxicity. With the recent large-scale implementation of annual lung cancer screening using low-dose chest CT (LDCT), gynecomastia is believed to be a frequent incidental finding on LDCT. A fully automated system for gynecomastia quantification from LDCT is presented in this paper. The whole breast region is first segmented using an anatomyorientated approach based on the propagation of pectoral muscle fronts in the vertical direction. The subareolar region is then localized, and the fibroglandular tissue within it is measured for the assessment of gynecomastia. The presented system was validated using 454 breast regions from non-contrast LDCT scans of 227 adult men. The ground truth was established by an experienced radiologist by classifying each breast into one of the five categorical scores. The automated measurements have been demonstrated to achieve promising performance for the gynecomastia diagnosis with the AUC of 0.86 for the ROC curve and have statistically significant Spearman correlation r=0.70 (p < 0.001) with the reference categorical grades. The encouraging results demonstrate the feasibility of fully automated gynecomastia quantification from LDCT, which may aid the early detection as well as the treatment of both gynecomastia and the underlying medical problems, if any, that cause gynecomastia.

Paper Details

Date Published: 27 February 2018
PDF: 8 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057524 (27 February 2018); doi: 10.1117/12.2293852
Show Author Affiliations
Shuang Liu, Cornell Univ. (United States)
Emily B. Sonnenblick, Icahn School of Medicine at Mount Sinai (United States)
Lea Azour, Icahn School of Medicine at Mount Sinai (United States)
David F. Yankelevitz, Icahn School of Medicine at Mount Sinai (United States)
Claudia I. Henschke, Icahn School of Medicine at Mount Sinai (United States)
Anthony P. Reeves, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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