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

NCT ID: NCT05024591

Last Updated: 2023-09-28

Study Results

Results pending

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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Recruitment Status

UNKNOWN

Total Enrollment

25008 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-02-01

Study Completion Date

2024-12-31

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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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.

Conditions

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Breast Cancer

Study Design

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Observational Model Type

COHORT

Study Time Perspective

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

Intervention Type DEVICE

• 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

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* 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
Minimum Eligible Age

40 Years

Maximum Eligible Age

100 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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Korea Health Industry Development Institute

OTHER_GOV

Sponsor Role collaborator

Kyung Hee University Hospital at Gangdong

OTHER

Sponsor Role lead

Responsible Party

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Jung Kyu Ryu,MD

MD, PhD, Principal Investigator

Responsibility Role 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

Site Status

Department of Radiology, Soonchunhyang University Hospital

Seoul, , South Korea

Site Status

Department of Radiology, Konkuk University Medical Center

Seoul, , South Korea

Site Status

Department of Radiology, Kyung Hee University Hospital at Gangdong

Seoul, , South Korea

Site Status

Department of Radiology, Nowon Eulgi Medical center

Seoul, , South Korea

Site Status

Countries

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South Korea

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.

Reference Type BACKGROUND
PMID: 33334578 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 32852536 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 35133093 (View on PubMed)

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.

Reference Type DERIVED
PMID: 41147858 (View on PubMed)

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.

Reference Type DERIVED
PMID: 40050619 (View on PubMed)

Other Identifiers

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oddie2

Identifier Type: -

Identifier Source: org_study_id

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