Breast Lesion Malignancy Evaluation

Purpose	To automate classification of breast lesions into probability/likelihood of malignancy at time of screening mammography, US
Tag(s)
Panel	Breast Imaging
Define-AI ID	19060004
Originator	Syam Reddy
Lead	Syam Reddy
Panel Chair	Beth Burnside
Panel Reviewers	Breast Imaging Panel
License	Creative Commons 4.0
Status	Public Comment
RadElement Set	RDES123

Value Proposition

Mammography, sonographic and MRI lesions may have many varying appearances that can be difficult to distinguish between benign and malignant. AI with deep learning can help differentiate lesions with low risk from high risk types or possibly predict cancer type and thereby more consistently help drive which patients require further diagnostic evaluation and ultimate biopsy. We can also add best practice recommendations to these lesions of concern. 

Narrative(s)

45 yr old high risk female presents for screening ultrasound. On her screening, a hypo echoic mass with partially circumscribed borders is seen. There is no vascularity and no posterior acoustic enhancement. AI views images or area of concern and give a percentage for a concerning lesion that would require further diagnostic evaluation. Based on lesion appearance and through deep learning, AI would give recommendation of 6 month follow up US as a best practice recommendation.

Workflow Description

Screening mammogram, US or MRI lesion images are sent to AI after selected as a possible abnormality by reading radiologist. AI reviews using prior deep learning experience for lesions analysis. A probability /likelihood percentage is given for each questioned lesions. Best practice guidance can be given for specific lesion type which would result in more appropriate diagnostic work up for patients.

Procedure(s)	Screening mammography to include screening ultrasound and Breast MRI
Indication	Breast cancer screening and early detection
Breast anatomy	{No prior surgery, no implants}
Age	40 years and older
Views	{Standard craniocaudal, Standard mediolateral}

Inputs

DICOM Study

Primary Outputs

Suspicious Breast Lesion Detection

Secondary Outputs

 

 

Breast Lesion Classification

 

 

 

 

Follow Up Plan Recommendation

 

 

 

Develop algorithm for those lesion biopsied to compare with pathology result data pulled from EMR and given recommended for concordance or treatment planning. Use lesion analysis to compare with pathology results for concordance.