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

Breast Cancer Clinical Trial

— ScreenTrustMRI  

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04832594
• Other study ID #: KSRAD001
• Status: Active, not recruiting
• Phase: N/A
• Start date: April 1, 2021
• Est. completion date: July 1, 2025

Study information

• Verified date: October 2023
• Source: Karolinska University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 2500
• Est. completion date: July 1, 2025
• Est. primary completion date: April 1, 2023
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 40 Years to 74 Years

Eligibility

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

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Intervention

Other:
• AI selection for supplemental breast MRI
An AI tool will generate scores used to determine eligibility. Women randomized to MRI will be examined in an MRI scanner.

Locations

Country	Name	City	State
Sweden	Karolinska University Hospital	Stockholm

Sponsors (4)

Lead Sponsor	Collaborator
Karolinska University Hospital	Bröstcancerförbundet, Sweden, KTH Royal Institute of Technology, Region Stockholm

Country where clinical trial is conducted

Sweden, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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
Secondary	Invasiveness	In situ and/or Invasive cancer	All diagnosed breast cancer within 27 months of study inclusion
Secondary	Histology	Ductal, Lobular, Mucinous, Tubular, Other	All diagnosed breast cancer within 27 months of study inclusion
Secondary	Lymph node metastasis	0 nodes, 1-3 nodes, 4 or more nodes	All diagnosed breast cancer within 27 months of study inclusion
Secondary	Tumor size	Size (in millimetre) for the invasive and the in situ component	All diagnosed breast cancer within 27 months of study inclusion
Secondary	Receptor status	ER positive/negative, PR positive/negative, HER2 positive/negative	All diagnosed breast cancer within 27 months of study inclusion
Secondary	Age	Age of the woman	At study inclusion
Secondary	Distribution of AI scores	Histogram, mean, median and dispersion measures for the AI Scores	At study inclusion
Secondary	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
Secondary	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
Secondary	Participant questionnaire	Participant questionnaire replies for MRI contraindications and for Breast cancer-related history	At study inclusion