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

Kyung Hee University Hospital at Gangdong25,008 enrolled

Overview

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.

1. 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. 2. 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. 3. 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. 4. 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. \[Set1\] 5. 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. \[Set 3\] 6. 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.

Study Type

OBSERVATIONAL

Enrollment

25,008

Conditions

Breast Cancer

Interventions

Lunit INSIGHT MMG CADe/x for medical imagingDEVICE

• 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

Eligibility

Sex: FEMALEMin age: 40 YearsMax age: 100 Years

Medical Language ↔ Plain English

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

Locations (5)

Department of Radiology, CHA bundang Medical Center

Seongnam-si, South Korea

Department of Radiology, Soonchunhyang University Hospital

Seoul, South Korea

Department of Radiology, Konkuk University Medical Center

Seoul, South Korea

Department of Radiology, Kyung Hee University Hospital at Gangdong

Seoul, South Korea

Outcomes

Primary Outcomes

• 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

Time frame: 12months after screening, 24months after screening

Secondary Outcomes

• 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.

Time frame: 12months after screening, 24 months after screening

Data from ClinicalTrials.gov

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