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 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

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 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

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.

Mammographic Breast Density Assessment

2013 ◽  
Vol 311 ◽  
pp. 518-523
Author(s):  
Chien Shun Lo ◽  
Si Wa Chan ◽  
San Kan Lee
Keyword(s):  
Breast Cancer ◽  
Breast Density ◽  
Early Stage ◽  
Characteristic Curve ◽  
Entropy Method ◽  
Mammographic Breast Density ◽  
Dense Area ◽  
Incidence And Mortality ◽  
High Mammographic Density ◽  
Density Assessment

In Taiwan, breast cancer has become the second leading type of cancerous diseases among women. The incidence and mortality rates keep rising, and mammography remains to be the only effective screening technique which is capable of detecting breast cancer at an early stage. High mammographic density is a strong risk factor for breast cancer. Based on BI-RADS categories, mammograms are classified into four categories (D1-D4) based on the percentage of dense area (PDA). However, reporting of breast density suffers from high inter and intra observer variability. Because the risk of breast cancer is at least three times greater in women with density (D3&D4) than in women with density D1, this paper proposes a local entropy method to identify the higher density (D3&D4) from (D1&D2) by two features. There are 406 mammograms with four categories collected for the test. The higher density (D3&D4) can be identified from lower density (D1&D2) in the correction of 100%. The Az of receiver operating characteristic curve of 0.9996 can be achieved.

Sign in / Sign up

Export Citation Format

Share Document