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

NCT05024591 | Unknown DMC

• 1 other identifier: oddie2
• Study Type: observational
• Target: 25,008
• Locations: 1 country
• Sites: 5

Brief Summary

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.

Conditions

Breast Cancer

Keywords

breast cancer; screening; AI-based CADe/x

Outcome Measures

Primary Outcomes (1)

• • 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 Outcomes (1)

• • Diagnostic accuracy and difference of the following comparison groups with or without AI

  12months after screening, 24 months after screening

Study Arms (1)

same as study population

Use of AI-based CADe/x by breast radiologists

Device: Lunit INSIGHT MMG CADe/x for medical imaging

Interventions

Lunit INSIGHT MMG CADe/x for medical imaging 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

same as study population

Eligibility Criteria

• Age: 40 Years - 100 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Participants of breast cancer screening in Korean women aged 40 years or older with average risk of breast cancer.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Kyung Hee University Hospital at Gangdong: lead
• Korea Health Industry Development Institute: collaborator

Study Sites (5)

Department of Radiology, CHA bundang Medical Center

Seongnam-si, South Korea

Location

Department of Radiology, Soonchunhyang University Hospital

Seoul, 04401, South Korea

Location

Department of Radiology, Konkuk University Medical Center

Seoul, South Korea

Location

Department of Radiology, Kyung Hee University Hospital at Gangdong

Seoul, South Korea

Location

Department of Radiology, Nowon Eulgi Medical center

Seoul, South Korea

Location

Related Publications (5)

• 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.

  PMID: 33334578 BACKGROUND

• 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.

  PMID: 32852536 BACKGROUND

• 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.

  PMID: 35133093 BACKGROUND

• 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. 2026 Jan;8(1):e250281. doi: 10.1148/ryai.250281.

  PMID: 41147858 DERIVED

• 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.

  PMID: 40050619 DERIVED

MeSH Terms

Conditions

Breast Neoplasms

Interventions

Diagnostic Imaging

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Intervention Hierarchy (Ancestors)

Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Yun-Woo Chang, MD, PhD

  Soonchunhyang University Hospital, Seoul

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD, PhD, Principal Investigator

Study Record Dates

• First Submitted: August 13, 2021
• First Posted: August 27, 2021
• Study Start: February 1, 2021
• Primary Completion: December 31, 2022
• Study Completion: December 31, 2024
• Last Updated: September 28, 2023

Record last verified: 2023-09

Data Sharing

• IPD Sharing: Will not share

Locations

KR (5)