Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM)

Study Overview
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Brief Summary
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This prospective study aims to generate real-world evidence on the overall benefits and disadvantages of using Lunit INSIGHT MMG AI based CADe/x for breast cancer detection in a population-based breast cancer screening program in Korea.

Detailed Description
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Several challenges have been identified in breast cancer screening: 1) Some breast cancer cases not identified through screening; 2) Excessive recalls for further testing; 3) Low sensitivity in dense breasts; 4) Inter-reader variability. AI-based CADe/x has been shown to improve radiologist performance and provides results equivalent or superior to those from radiologists alone. This multicenter, prospective study involves women who visit sites for breast cancer screening in Korea. Women eligible for national cancer screening in the relevant year who read the study participant recruitment brochure and read and sign the Participant Information Sheet and Informed Consent Form will be recruited into this study. Approximately 32,714 participants will be enrolled from February 2021 through December 2022 at five study sites in Korea. In Korea, a single radiologist performs mammogram readings. If recall is required (per usual care), further diagnostic work-up will be conducted to confirm cancer detected at screening. The national cancer registry databases will be reviewed in 2026 and 2027. Available findings will be recorded for all participants regardless of their screening status to identify study participants with breast cancer diagnosis within one year and within two years from screening. In primary outcome measurement, as part of the standard screening procedure, mammograms will be read and recorded by a breast radiologist without AI-CADe/x, and then with AI-based CADe/x. [Set1] In secondary outcome measurement, mammograms from the same participants as Set 1 will be read and recorded by a general radiologist without AI-based CADe/x, and then with AI-based CADe/x. [Set 2] In additional secondary outcome measurement, arbitration reading will be conducted by another breast radiologist without AI-based CADe/x for cases in which the reading results of the two radiologists without AI-based CADe/x in Set 1 and Set 2 are inconsistent. [Set 3] After completing the standard screening procedure in Set 1, several situational comparison groups [Set2 and Set3] for comparison the diagnostic accuracy will be performed independently and retrospectively The results from Set 2 and Set 3 will not impact the clinical decision(s) associated with the care of the study participants.

Official Title
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Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM): A Prospective, Multicenter Cohort Study

Conditions
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Breast Cancer

Intervention / Treatment
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* Device: Lunit INSIGHT MMG CADe/x for medical imaging


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Participants must meet all of the following inclusion criteria in order to be enrolled in the study: Be eligible for national cancer screening in the relevant year and visit the site for breast cancer screening Provide consent for study participation using the Informed Consent Form and complete a Participant information Sheet Exclusion Criteria: Participants who meet any of the following criteria will be excluded from the study: Has a history of or current breast cancer Is currently pregnant or plans to become pregnant in the next 12 months Has a history of breast surgery (mammoplasty or insertion of a foreign substance, such as paraffin or silicon) Has mammography for diagnostic purposes

Ages Eligible for Study
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Minimum Age: 40 Years
Maximum Age: 100 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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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| same as study population<br>Use of AI-based CADe/x by breast radiologists | Device: Lunit INSIGHT MMG CADe/x for medical imaging<br>* • A software that detects areas suspected of breast cancer using mammographic images, marks areas suspected of malignant lesions, and displays the probability of malignant lesions to assist with the interpreting physician's diagnosis<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| • Diagnostic accuracy with difference between breast radiologists with and without AI-based CADe/x | Diagnostic accuracy is assessed using cancer registry data as the reference group to calculate cancer detection rate [CDR], recall rate, sensitivity, positive predictive value | 12months after screening, 24months after screening | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| • Diagnostic accuracy and difference of the following comparison groups with or without AI | Between general radiologist with and without AI-based CADe/x Between breast radiologist arbitration reading and breast radiologist with AI-based CADe/x Between breast radiologist arbitration reading and general radiologist with AI-based CADe/x Between general radiologist with AI-based CADe/x and breast radiologist without AI-based CADe/x Between breast radiologist without AI-based CADe/x and stand-alone AI-based CADe/x Between general radiologist without AI-based CADe/x and stand-alone AI-based CADe/x Between breast radiologist with AI-based CADe/x and general radiologist with AI-based CADe/x Between breast radiologist without AI-based CADe/x and general radiologist without AI-based CADe/x Diagnostic accuracy is assessed using cancer registry data as the reference group to calculate CDR, recall rate, sensitivity, PPV, specificity, interval cancer rate, and AUROC. | 12months after screening, 24 months after screening | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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breast cancer, screening, AI-based CADe/x