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

Breast Cancer Clinical Trial

Official title:

Artificial Intelligence for breaST canceR scrEening in mAMmography (AI-STREAM): A Prospective, Multicenter Cohort Study

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05024591
• Other study ID #: oddie2
• Status: Active, not recruiting
• Start date: February 1, 2021
• Est. completion date: December 31, 2024

Study information

• Verified date: September 2023
• Source: Kyung Hee University Hospital at Gangdong
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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.

Description:

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 25008
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2022
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 40 Years to 100 Years

Eligibility

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

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Intervention

Device:
• 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

Locations

Country	Name	City	State
Korea, Republic of	Department of Radiology, CHA bundang Medical Center	Seongnam-si
Korea, Republic of	Department of Radiology, Konkuk University Medical Center	Seoul
Korea, Republic of	Department of Radiology, Kyung Hee University Hospital at Gangdong	Seoul
Korea, Republic of	Department of Radiology, Nowon Eulgi Medical center	Seoul
Korea, Republic of	Department of Radiology, Soonchunhyang University Hospital	Seoul

Sponsors (2)

Lead Sponsor	Collaborator
Kyung Hee University Hospital at Gangdong	Korea Health Industry Development Institute

Country where clinical trial is conducted

Korea, Republic of, 

References & Publications (3)

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	• 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	• 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.	12months after screening, 24 months after screening