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Breast density follow-up decision support system using deep convolutional models
Author(s): Sun Young Park; Dustin Sargent; David Richmond
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Paper Abstract

Breast cancer risk assessment relies on accurate classification of breast density, which is a key component of the ACR breast cancer screening recommendations for clinical decisions. The 5th edition of the BIRADS standard divides breast density into four categories, ranging from almost entirely fatty to extremely dense. High breast density (classes C and D) reduces the sensitivity of mammography, since the dense fibroglandular tissue can hide lesions, masses and other findings. Therefore, although the benefit of supplementary imaging in such cases has not been conclusively demonstrated, the ACR guidelines suggest additional screening for patients with high breast density. This creates an important treatment decision boundary between class B (scattered areas of fibroglandular density) and class C (heterogeneously dense). Unfortunately, the slightly abstract, qualitative nature of the class descriptions leads to significant inter- and intra-rater variation in breast density assessment. This is exacerbated by updates to the BIRADS standard that can cause recent breast density assessments to be incompatible with prior assessments for the same patient. Additionally, images from similar patients can vary significantly when taken with different devices or at sites with different acquisition protocols. To address these issues, we present a new deep learning algorithm combining three models that achieves accurate and objective breast density classification. The first model performs the normal four-class breast density classification, the second model performs a two-class low (A or B) vs. high (C or D) classification, and the third patch-based model focuses on improving the accuracy of the B and C categories. We present initial results from 9989 studies from a three-site dataset with BIRADS 4th and 5th edition ground truth.

Paper Details

Date Published: 13 March 2019
PDF: 9 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500G (13 March 2019); doi: 10.1117/12.2513047
Show Author Affiliations
Sun Young Park, IBM Watson Health (United States)
Dustin Sargent, IBM Watson Health (United States)
David Richmond, IBM Watson Health (United States)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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