scholarly journals Deep Learning for Mammographic Breast Density Assessment and Beyond

Radiology ◽  
2019 ◽  
Vol 290 (1) ◽  
pp. 59-60 ◽  
Author(s):  
Heang-Ping Chan ◽  
Mark A. Helvie
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Density Assessment

scholarly journals Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective

2017 ◽  
Vol 31 (4) ◽  
pp. 387-392 ◽  
Author(s):  
Aly A. Mohamed ◽  
Yahong Luo ◽  
Hong Peng ◽  
Rachel C. Jankowitz ◽  
Shandong Wu
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Density Assessment

scholarly journals Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Radiology ◽  
2019 ◽  
Vol 290 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Constance D. Lehman ◽  
Adam Yala ◽  
Tal Schuster ◽  
Brian Dontchos ◽  
Manisha Bahl ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Clinical Implementation ◽  
Density Assessment

Impact of a deep learning model to reduce variation and costs of federally mandated breast density legislation.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1550-1550
Author(s):  
Katherine Cavallo Hom ◽  
Brian Nicholas Dontchos ◽  
Sarah Mercaldo ◽  
Pragya Dang ◽  
Leslie Lamb ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Practice ◽  
Breast Density ◽  
Routine Clinical Practice ◽  
Dense Breast ◽  
Wilcoxon Test ◽  
Screening Tests ◽  
Mammographic Breast Density ◽  
Community Practice ◽  
Screening Mammograms

1550 Background: Dense breast tissue is an independent risk factor for malignancy and can mask cancers on mammography. Yet, radiologist-assessed mammographic breast density is subjective and varies widely between and within radiologists. Our deep learning (DL) model was implemented into routine clinical practice at an academic breast imaging center and was externally validated at a separate community practice, with both sites demonstrating high clinical acceptance of the model’s density predictions. The aim of this study is to demonstrate the influence our DL model has on prospective radiologist density assessments in routine clinical practice. Methods: This IRB-approved, HIPAA-compliant retrospective study identified consecutive screening mammograms without exclusion performed across three clinical sites, over two time periods: pre-DL model implementation (January 1, 2017 through September 30, 2017) and post-DL model implementation (January 1, 2019 through September 30, 2019). Clinical sites were as follows: Site A (the academic practice where the DL model was developed and was implemented in late 2017); Site B (an affiliated community practice which implemented the DL model in late 2017 and was used for external validation); and Site C (an affiliated community practice which was never exposed to the DL model). Patient demographics and radiologist-assessed mammographic breast densities were compared over time and across sites. Patient characteristics were evaluated using Wilcoxon test and Pearson’s chi-squared test. Multivariable logistic regression models evaluated the odds of a dense breast classification as a function of time period (pre-DL vs post-DL), race (White vs non-White) and site. Results: A total of 85,865 consecutive screening mammograms across the three clinical sites were identified. After controlling for age and race, adjusted odds ratios (aOR) of a mammogram being classified as dense at Site C compared to Site B before the DL model was implemented was 2.01 (95% CI 1.873, 2.157, p<0.001). This increased to 2.827 (95% CI 2.636, 3.032, p< 0.001) after DL implementation. The aOR of a mammogram being classified as dense at Site A after implementation compared to before implementation was 0.924 (95% CI 0.885, 0.964, p<0.001). Conclusions: Our findings suggest implementation of the DL model influences radiologist’s prospective density assessments in routine clinical practice by reducing the odds of a screening exam being categorized as dense. As a result, clinical use of our model could reduce downstream costs of supplemental screening tests and limit unnecessary high-risk clinic evaluations.[Table: see text]

Mammographic Breast Density Assessment: A Methods Study

2011 ◽  
Vol ` (`) ◽  
pp. 8-14 ◽  
Author(s):  
Lusine Yaghjyan ◽  
Susan M. Pinney ◽  
Mary C. Mahoney ◽  
Arthur R. Morton ◽  
Jeanette Buckholz
Keyword(s):  
Breast Density ◽  
Mammographic Breast Density ◽  
Density Assessment

scholarly journals Evaluation of Quantra Hologic Volumetric Computerized Breast Density Software in Comparison With Manual Interpretation in a Diverse Population

2018 ◽  
Vol 12 ◽  
pp. 117822341875929
Author(s):  
Gloria Richard-Davis ◽  
Brianna Whittemore ◽  
Anthony Disher ◽  
Valerie Montgomery Rice ◽  
Rathinasamy B Lenin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Breast Density ◽  
Gold Standard ◽  
Assessment Tool ◽  
Low Risk ◽  
Mammographic Breast Density ◽  
Cutoff Value ◽  
Density Assessment

Objective: Increased mammographic breast density is a well-established risk factor for breast cancer development, regardless of age or ethnic background. The current gold standard for categorizing breast density consists of a radiologist estimation of percent density according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) criteria. This study compares paired qualitative interpretations of breast density on digital mammograms with quantitative measurement of density using Hologic’s Food and Drug Administration–approved R2 Quantra volumetric breast density assessment tool. Our goal was to find the best cutoff value of Quantra-calculated breast density for stratifying patients accurately into high-risk and low-risk breast density categories. Methods: Screening digital mammograms from 385 subjects, aged 18 to 64 years, were evaluated. These mammograms were interpreted by a radiologist using the ACR’s BI-RADS density method, and had quantitative density measured using the R2 Quantra breast density assessment tool. The appropriate cutoff for breast density–based risk stratification using Quantra software was calculated using manually determined BI-RADS scores as a gold standard, in which scores of D3/D4 denoted high-risk densities and D1/D2 denoted low-risk densities. Results: The best cutoff value for risk stratification using Quantra-calculated breast density was found to be 14.0%, yielding a sensitivity of 65%, specificity of 77%, and positive and negative predictive values of 75% and 69%, respectively. Under bootstrap analysis, the best cutoff value had a mean ± SD of 13.70% ± 0.89%. Conclusions: Our study is the first to publish on a North American population that assesses the accuracy of the R2 Quantra system at breast density stratification. Quantitative breast density measures will improve accuracy and reliability of density determination, assisting future researchers to accurately calculate breast cancer risks associated with density increase.

scholarly journals Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad

Diagnostics ◽  
2017 ◽  
Vol 7 (2) ◽  
pp. 30 ◽  
Author(s):  
Stamatia Destounis ◽  
Andrea Arieno ◽  
Renee Morgan ◽  
Christina Roberts ◽  
Ariane Chan
Keyword(s):  
Breast Density ◽  
Mammographic Breast Density ◽  
The Us ◽  
Density Assessment

scholarly journals A deep learning method for classifying mammographic breast density categories

2017 ◽  
Vol 45 (1) ◽  
pp. 314-321 ◽  
Author(s):  
Aly A. Mohamed ◽  
Wendie A. Berg ◽  
Hong Peng ◽  
Yahong Luo ◽  
Rachel C. Jankowitz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Learning Method
Sign in / Sign up

Export Citation Format

Share Document