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