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Proceedings Paper

Volumetric versus area-based density assessment: comparisons using automated quantitative measurements from a large screening cohort
Author(s): Aimilia Gastounioti; Meng-Kang Hsieh; Lauren Pantalone; Emily F. Conant; Despina Kontos
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Paper Abstract

Mammographic density is an established risk factor for breast cancer. However, area-based density (ABD) measured in 2D mammograms consider the projection, rather than the actual volume of dense tissue which may be an important limitation. With the increasing utilization of digital breast tomosynthesis (DBT) in screening, there’s an opportunity to routinely estimate volumetric breast density (VBD). In this study, we investigate associations between DBT-VBD and ABD extracted from standard-dose mammography (DM) and synthetic 2D digital mammography (sDM) increasingly replacing DM. We retrospectively analyzed bilateral imaging data from a random sample of 1000 women, acquired over a transitional period at our institution when all women had DBT, sDM and DM acquired as part of their routine breast screening. For each exam, ABD was measured in DM and sDM images with the publicly available “LIBRA” software, while DBT-VBD was measured using a previously validated, fully-automated computer algorithm. Spearman correlation (r) was used to compare VBD to ABD measurements. For each density measure, we also estimated the within woman intraclass correlation (ICC) and finally, to compare to clinical assessments, we performed analysis of variance (ANOVA) to evaluate the variation to the assigned clinical BI-RADS breast density category for each woman. DBT-VBD was moderately correlated to ABD from DM (r=0.70) and sDM (r=0.66). All density measures had strong bilateral symmetry (ICC = [0.85, 0.95]), but were significantly different across BI-RADS density categories (ANOVA, p<0.001). Our results contribute to further elaborating the clinical implications of breast density measures estimated with DBT which may better capture the volumetric amount of dense tissue within the breast than area-based measures and visual assessment.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105742H (2 March 2018); doi: 10.1117/12.2293051
Show Author Affiliations
Aimilia Gastounioti, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Meng-Kang Hsieh, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Lauren Pantalone, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Emily F. Conant, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Despina Kontos, Perelman School of Medicine, Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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