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
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Breast Cancer
- Sponsor
- Karolinska University Hospital
- Enrollment
- 2500
- Locations
- 1
- Primary Endpoint
- Significantly Delayed Breast Cancer Detection per 1000 women
- Status
- Active, not recruiting
- Last Updated
- 2 years ago
Overview
Brief Summary
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
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.
Investigators
Fredrik Strand
Principal Investigator
Karolinska University Hospital
Eligibility Criteria
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
Outcomes
Primary Outcomes
Significantly Delayed Breast Cancer Detection per 1000 women
Time Frame: 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.
Composite end-point defined by either: 1. Interval Cancer, 2. Cancer with lymph node metastasis, 3. Cancer with invasive component larger than 15 mm
Secondary Outcomes
- Tumor size(All diagnosed breast cancer within 27 months of study inclusion)
- Distribution of AI scores(At study inclusion)
- MRI-detected breast cancer(Diagnosis during work-up within 2 months of the initial screening MRI)
- Invasiveness(All diagnosed breast cancer within 27 months of study inclusion)
- Age(At study inclusion)
- BI-RADS codes(At study inclusion and until end of 27 month follow-up)
- Participant questionnaire(At study inclusion)
- Histology(All diagnosed breast cancer within 27 months of study inclusion)
- Receptor status(All diagnosed breast cancer within 27 months of study inclusion)
- Lymph node metastasis(All diagnosed breast cancer within 27 months of study inclusion)
- Biopsy result(Diagnosis during work-up within 2 months of the MRI examination)