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Proceedings Paper

Fully automated breast density assessment from low-dose chest CT
Author(s): Shuang Liu; Laurie R. Margolies; Yiting Xie; David F. Yankelevitz; Claudia I. Henschke; Anthony P. Reeves
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Paper Abstract

Breast cancer is the most common cancer diagnosed among US women and the second leading cause of cancer death 1 . Breast density is an independent risk factor for breast cancer and more than 25 states mandate its reporting to patients as part of the lay mammogram report 2 . Recent publications have demonstrated that breast density measured from low-dose chest CT (LDCT) correlates well with that measured from mammograms and MRIs 3-4 , thereby providing valuable information for many women who have undergone LDCT but not recent mammograms. A fully automated framework for breast density assessment from LDCT is presented in this paper. The whole breast region is first segmented using an anatomy-orientated novel approach based on the propagation of muscle fronts for separating the fibroglandular tissue from the underlying muscles. The fibroglandular tissue regions are then identified from the segmented whole breast and the percentage density is calculated based on the volume ratio of the fibroglandular tissue to the local whole breast region. The breast region segmentation framework was validated with 1270 LDCT scans, with 96.1% satisfactory outcomes based on visual inspection. The density assessment was evaluated by comparing with BI-RADS density grades established by an experienced radiologist in 100 randomly selected LDCT scans of female subjects. The continuous breast density measurement was shown to be consistent with the reference subjective grading, with the Spearman’s rank correlation 0.91 (p-value < 0.001). After converting the continuous density to categorical grades, the automated density assessment was congruous with the radiologist’s reading in 91% cases.

Paper Details

Date Published: 3 March 2017
PDF: 10 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340R (3 March 2017); doi: 10.1117/12.2254151
Show Author Affiliations
Shuang Liu, Cornell Univ. (United States)
Laurie R. Margolies, Mount Sinai School of Medicine (United States)
Yiting Xie, Cornell Univ. (United States)
David F. Yankelevitz, Mount Sinai School of Medicine (United States)
Claudia I. Henschke, Mount Sinai School of Medicine (United States)
Anthony P. Reeves, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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