Breast Cancer Risk Assessment

Purpose	To create reliable standard for breast cancer risk based on multivariable information.
Tag(s)
Panel	Breast Imaging
Define-AI ID	19060003
Originator	Syam Reddy
Lead	Syam Reddy
Panel Chair	Beth Burnside
Panel Reviewers	Breast Imaging
License	Creative Commons 4.0
Status	Public Comment
RadElement Set	RDES120

                

Clinical Implementation

Value Proposition

Risk stratification for patients allows radiologists to provide focused additional screening and prevention efforts where necessary to help reduce risk and less surveillance in those patients with lower risk to allow for better cost effective healthcare. Currently, there are numerous variables that need to be assessed for modeling a risk assessment tool. Current models have varying strengths that rely on patient’s own personal history or some that rely more on family history of breast or ovarian cancer. To date, no clinical risk assessment models incorporate breast density, known 4 to 6 fold increased risk factor for developing breast cancer. AI can help incorporate breast density and currently available risk assessment tools (inclusive of patient’s personal and family history) to improve overall risk assessment in individualized patients. Additionally, deep learning applied to breast cancer patient data may lead to new risk factors to make the model more robust. In light recent breast density legislation, patients are now more so than ever before, wanting to better understand their individualized breast cancer risk and optimal screening strategy.

Narrative(s)

A 50 year old patient decides to have her first baseline mammogram and presents with extremely dense breasts. She has a family history of mother and sister with diagnosed with breast cancer at 40 and 50 years, respectively. AI tools would incorporate this information, as well as other variables from EMR, including breast density from the mammogram or radiology report, and patient in-take questionnaire. The information would run through varying pre-existing risk assessment calculators as well as use deep learning to create a more robust and accurate breast cancer risk assessment tool. Information could then be used for tailoring individualized surveillance plans and risk reduction strategies where appropriate.

Workflow Description

Standard mammography views to assess quantitative breast density, historical data from EMR (ie: age, menopausal status) and answers to questions from the patient smart tablet (ie: smoker, parity etc) would be sent to AI engine which would use deep learning and various risk calculators. DICOM header output information from AI engine to PACS would be given for documentation and incorporated into the radiology report interpretation for added value. Based on risk assessment, recommendation for additional screening methods or risk reduction strategies would be performed. Additionally, tailoring personalized screening algorithms including interval of recommended screening, and/or best supplemental screening methods will be studied.

Considerations for Dataset Development

 

Procedure(s)	Screening mammography
Indication	Breast cancer screening and early detection
Breast Anatomy	{No prior surgery, no implants}
Age	40 years and older
View(s)	{Standard craniocaudal, standard mediolateral}
Prior radiation exposure	varied
EMR	Historical data from EMR; parity, estrogen use, menopausal status, BMI/obesity, age
History	{family cancer history, menarche, personal cancer history, etc.}
Risk assessment calculators	{Gail, Tyrer Cuzick, BRCA pro, etc.}

Technical Specifications

Inputs

DICOM Study

Procedure	Screening mammography
Views	Standard Craniocaudal and mediolateral
Data Type	DICOM
Modality	MAM
Body Region	Chest
Anatomic Focus	Breast

Primary Outputs

Risk Assessment for Breast Cancer

RadElement ID	RDE784
Definition	Personal lifetime risk assessment for breast cancer
Data Type	Numeric
Value Set	[0,1]
Units	Probability of developing breast cancer

Future Development Ideas

• Include risk assessment score for each common type of breast cancer.
• Analyze cases over time to better learn the correlations with breast cancer such as density to cancer occurrence, types of cancer with various risk factors, and demographics to cancer occurrence among others.