Breast Density Quantification

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

TOUCH-AI ID

TAI-061902

Originator

Sarah Eskreis-Winkler, Todd Hertzberg

Panel Chair

Elizabeth Burnside

Panel Reviewers

Breast Imaging Panel

License

Creative Commons 4.0
Status Released For Commenting
                               

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


Primary Input

DICOM Study

Procedure

Screening Mammography
Views Bilateral CC and MLO mammography images

Data Type

DICOM
Modality MAM
Body Region Breast
Anatomy Breast
Pharmaceutical N/A
Scenario N/A

Secondary Input

DICOM Study

Procedure

Screening Tomosynthesis
Views Bilateral CC and MLO tomosynthesis views from the entire 3D dataset

Data Type

DICOM
Modality MAM
Body Region Breast
Anatomy Breast
Pharmaceutical N/A
Scenario N/A

Primary Outputs

BI-RADS Breast Composition Category

RadElement ID

Definition

Assign a BI-RADS breast composition category

Data Type

Categorical

Value Set

0-Unknown
1-Almost entirely fatty
2-Scattered areas of fibroglandular density
3-Heterogeneously dense
4-Extremely dense

Units

N/A

Secondary Outputs

Aggregate Numeric Breast Density Score

RadElement ID

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

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.