Using AI to Select Women for Supplemental MRI in Breast Cancer Screening

Study Overview
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Brief Summary
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This is a prospective clinical trial aiming to determine the ability of an AI pipeline to identify women who would benefit from supplemental MRI in terms of decreasing the number of cancers having a significantly delayed detection

Detailed Description
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All women attending mammography screening at Karolinska University Hospital will have their mammograms analyzed by AI (Figure 1). The specific AI-implementation (AI tool) in this study is a result of AI predictions from three equally weighted component AI models analyzing mammograms: (i) masking predictor, (ii) risk predictor and (iii) cancer signs predictor (by one commercial CAD model and one in-house academic CAD model); the age of the woman is also taken into account by multiplying the score with (110-age)/70. The purpose of the age factor is to attain a relatively similar proportion of MRI exams in the lower and higher age groups. The aim of the AI tool is to identify women with the highest probability of having a delay in cancer detection, i.e., having had a false negative screening mammogram. An AI-based framework has been developed by researchers at Karolinska Institute (led by Dr. Fredrik Strand) and Royal Institute of Technology (led by Dr: Kevin Smith). The specific AI-implementation (AI tool) in this study is a result of AI predictions from three equally weighted component AI models analyzing mammograms: (i) masking predictor, (ii) risk predictor and (iii) cancer signs predictor (by one commercial CAD model and one in-house academic CAD model); the age of the woman is also taken into account by multiplying the score with (110-age)/70. The purpose of the age factor is to attain a relatively similar proportion of MRI exams in the lower and higher age groups. The aim of the AI tool is to identify women with the highest probability of having a delay in cancer detection, i.e., having had a false negative screening mammogram. The specific AI tool and its settings will remain the same during the study. For each examination, the AI tool will produce an AI Joint Score and an AI Masking Score. The AI Masking Score cut-off point was defined by the median of examinations collected during the initial period of March 1 to March 24, 2021. The cut-off point of the AI Joint Score was defined by the 92nd percentile of the initial population. Women meeting these criteria will be invited to the study, and randomized to MRI or no-MRI (standard-of-care). A Signa Premier 3T MRI scanner from GE Healthcare will be used. The MRI protocol will contain a T2-weighted Dixon sequence and a T1-weighted dynamic contrast enhanced series, and will remain the same through the course of the study. All MRI exams will be assessed by two radiologists, where the second reader will have access to the assessment of the first reader. In case of disagreement, a consensus discussion between two radiologists will be held. The MRI exams will be assessed according to BI-RADS, and follow-up will depend on the BI-RADS category (Figure 2). Women with BI-RADS 1-2 will have no further diagnostics and will be sent a 'healthy letter'. Women with BI-RADS 3 to 5 will be recalled for 2nd look ultrasound. Women with BI-RADS 4-5 will be included in the regular process for established cancer suspicion and be discussed in a multidisciplinary team conference. For women with BI-RADS 3, the follow-up will be handled within the breast radiology unit.

Official Title
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Image Analysis With Artificial Intelligence to Increase Precision in Breast Cancer Screening - the ScreenTrust MRI Substudy: a Prospective Trial of AI to Select Women for Supplemental Screening MRI

Conditions
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Breast Cancer

Intervention / Treatment
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* Other: AI selection for supplemental breast MRI


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Complete four-view screening mammography examination Exclusion Criteria: Women in surveillance program referred from the hereditary cancer unit Breast implants Prior breast cancer Breast feeding MRI contraindication requiring radiologist assessment AI Tool unable to process mammograms due to technical reason

Ages Eligible for Study
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Minimum Age: 40 Years
Maximum Age: 74 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Screening
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: For each screening mammography examination, the AI tool will produce an AI Joint Score and an AI Masking Score. Women having an AI Masking Score above the threshold and an AI Joint Score above the threshold will be invited to the study unless they met exclusion criteria. Women who decide to participate, will be randomized to MRI or no-MRI (standard-of-care).
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Supplemental MRI<br>Women randomized to MRI will be examined using a shortened MRI protocol on a Signa Premier 3T MRI scanner. The MRI examination will be reviewed by two radiologists and assigned BI-RADS score. Appropriate clinical work-up will follow according to the BI-RADS score. BI-RADS 3 or higher at initial MRI will be recalled for a second look ultrasound. | Other: AI selection for supplemental breast MRI<br>* An AI tool will generate scores used to determine eligibility. Women randomized to MRI will be examined in an MRI scanner.<br>|
| No Intervention: No MRI (standard-of-care)<br>Standard-of-care. Both arms will have had a regular screening mammography examination prior to randomization. The No MRI arm will have no further intervention. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Significantly Delayed Breast Cancer Detection per 1000 women | Composite end-point defined by either: 1. Interval Cancer, 2. Cancer with lymph node metastasis, 3. Cancer with invasive component larger than 15 mm | Until 27 months from study inclusion (includes cancer detected at subsequent screening within this time frame). Cancer detected at the initial screening mammography or MRI shall not be included. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| MRI-detected breast cancer | Breast cancer detected at the initial screening MRI for women in the Intervention arm of the study | Diagnosis during work-up within 2 months of the initial screening MRI | 
| Invasiveness | In situ and/or Invasive cancer | All diagnosed breast cancer within 27 months of study inclusion | 
| Histology | Ductal, Lobular, Mucinous, Tubular, Other | All diagnosed breast cancer within 27 months of study inclusion | 
| Lymph node metastasis | 0 nodes, 1-3 nodes, 4 or more nodes | All diagnosed breast cancer within 27 months of study inclusion | 
| Tumor size | Size (in millimetre) for the invasive and the in situ component | All diagnosed breast cancer within 27 months of study inclusion | 
| Receptor status | ER positive/negative, PR positive/negative, HER2 positive/negative | All diagnosed breast cancer within 27 months of study inclusion | 
| Age | Age of the woman | At study inclusion | 
| Distribution of AI scores | Histogram, mean, median and dispersion measures for the AI Scores | At study inclusion | 
| BI-RADS codes | For each MRI examination, the BI-RADS code for fibroglandular volume, background enhancement and breast lesions | At study inclusion and until end of 27 month follow-up | 
| Biopsy result | Pathology assessment of biopsy: normal tissue, benign lesion, cancer in situ, invasive cancer | Diagnosis during work-up within 2 months of the MRI examination | 
| Participant questionnaire | Participant questionnaire replies for MRI contraindications and for Breast cancer-related history | At study inclusion | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial Intelligence