Abstract OT3-18-03: The PRAIM study: A prospective multicenter observational study of an integrated Artificial Intelligence system with live monitoring

Abstract Background. Several retrospective studies have illustrated the potential clinical benefit of artificial intelligence (AI) systems for breast cancer screening. Some systems optimize normal mammography examination triaging, while others aim to improve cancer detection. However, no AI system has shown specificity high enough to replace human radiologists, suggesting that AI should play a different role in the breast screening pathway. The decision-referral approach is a promising alternative that has demonstrated the most potential to improve radiologist screening sensitivity and specificity while reducing workload. This collaborative human-AI approach combines AI pre-screening to triage normal examinations and post-screening to prevent missed cancers. The actual performance of decision-referral, including the interaction with human radiologists, can ideally be evaluated in a prospective real-world setting. Trial design. The PRAIM (PRospective, multicenter observational study of an integrated AI system with live monitoring to support breast cancer screening) study (German Trial Register: DRKS00027322) is a prospective controlled observational non-inferiority study to compare the use of CE-marked screening software including AI support (Vara) via the decision-referral approach, with standard screening for women participating in the German breast cancer screening program. Ethics approval was obtained from the University of Lübeck Research Ethics Committee (22-043). Examinations assessed by readers using Vara are compared to examinations without Vara (control). Eligibility criteria and target accrual. Women ages 50 to 69 years old undergoing biennial breast cancer screening within the national screening program are eligible for inclusion. We expect the inclusion of approximately 400,000 women within the inclusion period of 1.5 years. Statistical methods. The primary outcome is the screen-detected cancer rate, defined as biopsy-confirmed cancer diagnoses per 1000 screening examinations. For each screening site, rates over the prospective observation period are calculated for examinations read with AI and without. To control for systematically different screen-detected cancer rates across screening sites, a historical 5-year rate is computed for each site and subtracted from the corresponding prospective rates. Non-inferiority of the screen-detected cancer rate for the AI group compared to the control group is evaluated with a weighted, mixed-effects linear regression model. AI is considered as non-inferior if the lower bound of the two-sided 95 % confidence interval for the estimated difference in screen-detected cancer rates of AI and non-AI group is not below -10 %, which corresponds to a deviation of -0.6 screen-detected cancers per 1000 examinations. Citation Format: Danalyn Byng, Nora Eisemann, Dominik Schüler, Stefan Bunk, Christian Leibig, Moritz Brehmer, Susanne Elsner, Alexander Katalinic. The PRAIM study: A prospective multicenter observational study of an integrated Artificial Intelligence system with live monitoring [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT3-18-03.