Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM)
NCT ID: NCT05024591
Last Updated: 2023-09-28
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
25008 participants
OBSERVATIONAL
2021-02-01
2024-12-31
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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same as study population
Use of AI-based CADe/x by breast radiologists
Lunit INSIGHT MMG CADe/x for medical imaging
• 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
Interventions
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Lunit INSIGHT MMG CADe/x for medical imaging
• 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 Criteria
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Inclusion Criteria
* Provide consent for study participation using the Informed Consent Form and complete a Participant information Sheet
Exclusion Criteria
* 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
40 Years
100 Years
FEMALE
No
Sponsors
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Korea Health Industry Development Institute
OTHER_GOV
Kyung Hee University Hospital at Gangdong
OTHER
Responsible Party
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Jung Kyu Ryu,MD
MD, PhD, Principal Investigator
Principal Investigators
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Yun-Woo Chang, MD, PhD
Role: STUDY_DIRECTOR
Soonchunhyang University Hospital, Seoul
Locations
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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
Department of Radiology, Nowon Eulgi Medical center
Seoul, , South Korea
Countries
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References
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Kim HE, Kim HH, Han BK, Kim KH, Han K, Nam H, Lee EH, Kim EK. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health. 2020 Mar;2(3):e138-e148. doi: 10.1016/S2589-7500(20)30003-0. Epub 2020 Feb 6.
Salim M, Wahlin E, Dembrower K, Azavedo E, Foukakis T, Liu Y, Smith K, Eklund M, Strand F. External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms. JAMA Oncol. 2020 Oct 1;6(10):1581-1588. doi: 10.1001/jamaoncol.2020.3321.
Chang YW, An JK, Choi N, Ko KH, Kim KH, Han K, Ryu JK. Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x. J Breast Cancer. 2022 Feb;25(1):57-68. doi: 10.4048/jbc.2022.25.e4. Epub 2022 Jan 6.
Chang YW, Ryu JK, An JK, Choi N, Park YM, Ko KH. Breast Cancers Detected and Missed by AI-CAD: Results from the AI-STREAM Trial. Radiol Artif Intell. 2025 Oct 28:e250281. doi: 10.1148/ryai.250281. Online ahead of print.
Chang YW, Ryu JK, An JK, Choi N, Park YM, Ko KH, Han K. Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study. Nat Commun. 2025 Mar 6;16(1):2248. doi: 10.1038/s41467-025-57469-3.
Other Identifiers
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oddie2
Identifier Type: -
Identifier Source: org_study_id
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