Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM)
Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM): A Prospective, Multicenter Cohort Study
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
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 Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Yun-Woo Chang, MD, PhD
- Phone Number: 82-02-710-3250
- Email: ywchang@schmc.ac.kr
Study Contact Backup
- Name: Jungkyu Ryu, MD, PhD
- Email: oddie2@naver.com
Study Locations
-
-
-
Seongnam-si, Korea, Republic of
- Department of Radiology, CHA bundang Medical Center
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Seoul, Korea, Republic of, 04401
- Department of Radiology, Soonchunhyang University Hospital
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Seoul, Korea, Republic of
- Department of Radiology, Konkuk University Medical Center
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Seoul, Korea, Republic of
- Department of Radiology, Kyung Hee University Hospital at Gangdong
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Seoul, Korea, Republic of
- Department of Radiology, Nowon Eulgi Medical center
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Prospective
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
same as study population
Use of AI-based CADe/x by breast radiologists
|
• 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
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
• 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
|
12months after screening, 24months after screening
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
• Diagnostic accuracy and difference of the following comparison groups with or without AI
Time Frame: 12months after screening, 24 months after screening
|
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. |
12months after screening, 24 months after screening
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Study Director: Yun-Woo Chang, MD, PhD, Soonchunhyang University Hospital, Seoul
Publications and helpful links
General 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.
- 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. Erratum In: J Breast Cancer. 2022 Apr;25(2):147.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- oddie2
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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