Dr. David Richmond Profile

Dr. David Richmond

at IBM Watson Health

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Proceedings Article | 13 March 2019 Presentation + Paper

Breast density follow-up decision support system using deep convolutional models

Sun Young Park, Dustin Sargent, David Richmond

Proceedings Volume 10950, 109500G (2019) https://doi.org/10.1117/12.2513047

KEYWORDS: Breast, Data modeling, Image classification, Binary data, Decision support systems, Breast cancer, Tissues, Mammography

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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 5^th 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 4^th and 5^th edition ground truth.

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