Breast CancerNCT05024591
Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM)
An observational study of Breast Cancer, run by Kyung Hee University Hospital at Gangdong at 5 sites in South Korea.
Trial 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. Read more about this study
- Who is it for?
- Ages 40 to 100 years, women only
- Conditions the study is for
- Breast Cancer
- What is being tested?
- Lunit INSIGHT MMG CADe/x for medical imaging
- Type of treatment
- Medical device
- How many people
- 25,008 participants
- Where
- 5 sitesSouth Korea
- Study dates
- 1 Feb 2021to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 100 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Participants of breast cancer screening in Korean women aged 40 years or older with average risk of breast cancer.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
5 sites in 1 country.
5 sites in 1 country
South Korea5 sites
Department of Radiology, CHA bundang Medical Center
Seongnam-si
Department of Radiology, Soonchunhyang University Hospital
Seoul, 04401
Department of Radiology, Konkuk University Medical Center
Seoul
Department of Radiology, Kyung Hee University Hospital at Gangdong
Seoul
Department of Radiology, Nowon Eulgi Medical center
Seoul
About this study
Official title: Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM): A Prospective, Multicenter Cohort Study
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.
Read the detailed description
- 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.
- Conditions
- Breast Cancer
- Being tested
- Lunit INSIGHT MMG CADe/x for medical imaging
- Keywords
- breast cancer, screening, AI-based CADe/x
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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
| Study group | Role | What they receive |
|---|---|---|
| same as study population | Use of AI-based CADe/x by breast radiologists Receives: Device: Lunit INSIGHT MMG CADe/x for medical imaging |
What is being measured
The primary outcome is the main question the study is designed to answer.
• 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
Measured: 12months after screening, 24months after screening
Secondary outcomes (1)
• 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.
Measured: 12months after screening, 24 months after screening
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Aug 2021
- Study start1 Feb 2021
- Primary completion31 Dec 2022
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 25,008 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kyung Hee University Hospital at Gangdong
- Sponsor type
- University, hospital or other
- Collaborators
- Korea Health Industry Development Institute
- Responsible party
- Principal investigator, Jung Kyu Ryu,MD, Kyung Hee University Hospital at Gangdong
- Investigators
- Yun-Woo Chang, MD, PhD, Study director, Soonchunhyang University Hospital, Seoul
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05024591
- Sponsor's protocol ID
- oddie2
- Status last verified
- Sep 2023
- First submitted
- 13 Aug 2021
- Last updated
- 28 Sep 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 33334578Salim 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.
PubMed 32852536Chang 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.
PubMed 35133093Chang 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.
PubMed 41147858Chang 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.
PubMed 40050619
Study at a glance
From the registry record, last updated by the sponsor on 28 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM): A Prospective, Multicenter Cohort Study
- ClinicalTrials.gov ID
- NCT05024591
- Acronym / sponsor ID
- oddie2
- Conditions
- Breast Cancer
- Keywords
- breast cancer, screening, AI-based CADe/x
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 25,008 participants (actual)
- Lead sponsor
- Kyung Hee University Hospital at GangdongUniversity, hospital or other
- Collaborators
- Korea Health Industry Development Institute
- Study start
- 1 Feb 2021 (actual)
- Primary completion
- 31 Dec 2022 (actual)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 27 Aug 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- 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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| same as study population | – | Device: Lunit INSIGHT MMG CADe/x for medical imagingUse of AI-based CADe/x by breast radiologists |
Who may qualify
- Ages
- 40 to 100 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Participants of breast cancer screening in Korean women aged 40 years or older with average risk of breast cancer.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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 | Device | same as study population |
Primary outcome
• Diagnostic accuracy with difference between breast radiologists with and without AI-based CADe/x
Time frame: 12months after screening, 24months after screening
Diagnostic accuracy is assessed using cancer registry data as the reference group to calculate cancer detection rate [CDR], recall rate, sensitivity, positive predictive value
1 secondary outcome
• Diagnostic accuracy and difference of the following comparison groups with or without AI
Time frame: 12months after screening, 24 months after screening
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Sep 2023.
- Background: PubMed 33334578Kim 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.
- Background: PubMed 32852536Salim 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.
- Background: PubMed 35133093Chang 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.
- Derived from this study: PubMed 41147858Chang 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.
- Derived from this study: PubMed 40050619Chang 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Yun-Woo Chang, MD, PhD study director
Soonchunhyang University Hospital, Seoul
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by Kyung Hee University Hospital at Gangdong at 5 sites in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 28 Sep 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
• Diagnostic accuracy with difference between breast radiologists with and without AI-based CADe/x (time frame: 12months after screening, 24months after screening). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05024591, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Sep 2023.