Classifying Suspicious Microcalcifications
| Purpose | To automate classification of breast microcalcifications into categories based on level of suspicion at time of diagnostic mammography |
| Tag(s) |
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| Panel | Breast Imaging |
| Define-AI ID | 18060001 |
| Originator | Christoph Lee |
| Panel Chair | Elizabeth Burnside |
| Panel Reviewers | Breast Imaging Panel |
| License | |
| Status | Published |
Clinical Implementation
Value Proposition
Microcalcifications identified on screening mammography may signify ductal carcinoma in situ (DCIS). However, the majority of microcalcifications are eventually found to be benign, and thus constitute a leading cause of false-positive screens and benign biopsies. There is variability in radiologist interpretation and BI-RADS assessment of microcalcifications at the time of diagnostic imaging (spot magnification evaluation of microcalcifications). AI can help improve accuracy and use quantitative imaging features to more accurately categorize microcalcifications by level of suspicion for DCIS, potentially decreasing the rate of unnecessary benign biopsies.Narrative(s)
A 40-year-old female presents for her baseline screening mammogram and is found to have a group of microcalcifications in the left breast. She is recalled from screening and undergoes standard diagnostic imaging with spot magnification views in the cranial-caudal (CC) and mediolateral (ML) projections. The AI algorithm provides the radiologist with an automated interpretation of BI-RADS categorization as well as a continuous numerical risk score at the time of the interpretation to help the radiologist provide the most suitable clinical recommendation.Workflow Description
Spot magnification mammography images obtained at diagnostic workup are sent from PACS to the AI engine. Images are analyzed by the AI engine, and the areas of microcalcification are given a BI-RADS classification and a numerical malignancy risk score. A message is sent to PACS from the engine with this information that can then be used by the interpreting radiologist to make a final assessment and recommendation.Considerations for Dataset Development
Procedures(s): Diagnostic mammography
View(s): Spot magnification CC and ML mammography images
Age: over 40 years
Breast Anatomy: no prior surgery or implants
Technical Specifications
Inputs
DICOM Study
| Procedure | Diagnostic mammography |
| Views | Spot magnification CC and ML mammography images |
| Data Type | DICOM |
| Modality | Mammo |
| Body Region | Chest |
| Anatomic Focus | Breast |
Primary Outputs
Suspicious Microcalcification Detection
| RadElement ID | |
| Definition | Identify suspicious microcalcifications |
| Data Type | Categorical |
| Value Set | 0-Unknown 1-Suspicious microcalcification present 2-Suspicious microcalcification absent |
| Units | N/A |
Probability of malignant microcalcification
| RadElement ID | |
| Definition | For a selected microcalcification, identify the probability of malignancy |
| Data Type | Numeric |
| Value Set | [0,1] 0-Benign 1-Malignant |
| Units | N/A |
Future Development Ideas
Develop a robust algorithm to handle microcalcification evaluations for screening images. Consider aggregating malignancy scoring based on evaluation of magnification and screening.Related Datasets
The Cancer Imaging Archive (TCIA)
Know of more related datasets? Please let us know