Breast Density

Purpose	To automate the assessment of breast density on digital mammography and digital breast tomosynthesis by developing an AI tool that: (1) assigns a BI-RADS® breast composition category, (2) assigns a composite numeric breast density score, and (3) provides regional breast density numeric sub-scores.
Tag(s)
Panel	Breast Imaging
Define-AI ID	19060012
Originator	Sarah Eskreis-Winkler, Todd Hertzberg
Panel Chair	Elizabeth Burnside
Panel Reviewers	Breast Imaging Panel
License	Creative Commons 4.0
Status	Public Comment
RadElement Set	RDES75

                               

Clinical Implementation

Value Proposition

Recent federal legislation requires that breast density information be included in all mammography reports. Breast density is an independent risk factor for breast cancer and can decrease the sensitivity of mammography.  Reliable breast density assessment is needed to identify women who may benefit from additional breast cancer screening.  However, qualitative mammographic breast density assessment is subjective and has high inter-reader variability. Automated categorization of breast density on mammography and tomosynthesis could reduce this subjective variability.  Auto-populating breast density assessment in the radiology report could also generate a modest time and cost savings.

Narrative(s)

A 40 year old women presents for her first screening mammogram.  An automated (AI-based) assessment algorithm generates a numeric breast density score and a BI-RADS breast composition category.  This results in a more standardized method of determining which patients should be counseled about the benefits of additional screening exams (such as ultrasound or breast MRI). The numeric breast density score could be further adjusted to age- and/or weight-matched controls.

Workflow Description

Digital data from standard mammographic and tomosynthesis breast exams are sent to a server, which can be accessed by the AI algorithm.  The algorithm then analyzes the data and dynamically generates breast density information for the interpreting radiologist, as well as automatically populating appropriate information into the radiologist’s report.

Considerations for Dataset Development

 

Procedure	Screening mammography and screening tomosynthesis
Views	Bilateral CC and MLO mammography views; bilateral CC and MLO tomosynthesis views from the entire 3D dataset
Age	over 40 years
Breast Anatomy	No exclusion criteria

Technical Specifications

Inputs

DICOM Study

Technical Specifications

Inputs

 

DICOM Study

 

Secondary Inputs

 

DICOM Study

Primary Outputs

 

BI-RADS Breast Composition Category

 

Secondary Outputs

 

Aggregate Numeric Breast Density Score

Tertiary Outputs

 

Regional Breast Density Information

Technical Specifications

Inputs

DICOM Study

Procedure	Screening Mammography
Views	Bilateral CC and MLO mammography images
Data Type	DICOM
Modality	MAM
Body Region	Chest
Anatomic Focus	Breast

 

Secondary Inputs

 

DICOM Study

Procedure	Screening Tomosynthesis
Views	Bilateral CC and MLO tomosynthesis views from the entire 3D dataset
Data Type	DICOM
Modality	MAM
Body Region	Chest
Anatomic Focus	Breast
Pharmaceutical	N/A
Scenario	N/A

Primary Outputs

 

BI-RADS Breast Composition Category

RadElement ID	RDES75
Definition	Assign a BI-RADS breast composition category
Data Type	Categorical
Value Set	0 -Unknown 1a-The breasts are almost entirely fatty. 2b-There are scattered areas of fibroglandular density. 3c-The breasts are heterogeneously dense, which may obscure small masses. 4d-The breasts are extremely dense, which lowers the sensitivity of mammography.
Units	N/A

 

Secondary Outputs

Aggregate Numeric Breast Density Score

RadElement ID	RDES75
Definition	Assign an aggregate numeric breast density score
Data Type	Numeric
Value Set	[0,1]
Units	N/A

Tertiary Outputs

Regional Breast Density Information

RadElement ID	RDES75
Definition	Regional breast density score
Data Type	Numeric
Value Set	List a score [0-1] for each region. For example: Right breast, upper outer quadrant, posterior third depth
Units	N/A

Future Development Ideas

The numeric scores outlined above could facilitate research into (1) the natural progression of breast density with age, (2) the identification of density change patterns likely to develop into cancer, and (3) the identification of density change patterns likely to be masking cancer.

Related Datasets

No known related public datasets at this time,  please alert us if you know of any.