Classifying Suspicious Microcalcifications

Purpose

To automate classification of breast microcalcifications into categories based on level of suspicion at time of diagnostic mammography

Tag(s)

 

Panel

Breast Imaging

TOUCH-AI ID

TAI-06180001

Originator

Christoph Lee

Panel Chair

Elizabeth Burnside

Panel Reviewers

Breast Imaging Panel

License

Creative Commons 4.0

Status Public Commenting
                               

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

RDE204

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

RDE205

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.

Public Commenting


Use cases are meant to be a primary vehicle for distributing clinical information to the developer community. They pinpoint precise scenarios within radiology workflows where potential automation could add noticeable value and establish standards for interpreting and passing corresponding common data elements. Implementing effective standards requires the perspective from all stakeholders. So to that end, we encourage your feedback on use cases.

To submit comments, please email DSIUseCases@acr.org with the use case title(s) and relevant comments by January 1, 2019. If more convenient, you may also download this use case and comment directly on the PDF. Just attach the PDF copy on the email.