Breast Cancer
Conditions
Keywords
breast cancer, screening, AI-based CADe/x
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.
Detailed 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.
Interventions
DEVICELunit 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
Sponsors
Korea Health Industry Development Institute
Kyung Hee University Hospital at Gangdong
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
40 Years to 100 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| • Diagnostic accuracy with difference between breast radiologists with and without AI-based CADe/x | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| • Diagnostic accuracy and difference of the following comparison groups with or without AI | 12months after screening, 24 months after screening | * 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. |
Countries
South Korea
Outcome results
None listed