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A Trial Comparing Screening Mammography With and Without Assistance From Artificial Intelligence for Breast Cancer Detection and Recall Rates in Adult Patients
NCT06934239 · Jonsson Comprehensive Cancer Center
In plain English
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Official title
A Randomized Controlled Trial Comparing Screening Mammography With and Without Assistance From Artificial Intelligence for Breast Cancer Detection and Recall Rates in Adult Patients
About this study
During the RCT the AI support tool will be randomized to be turned on or off (1:1) at the mammography exam level. Patients who return for screening exams in year 2 of recruitment will be randomized again (e.g., they will not retain their prior randomization). Radiologists will not be able to sort exams based on AI availability or AI scores. Randomizing by exam level will ensure that we capture a substantial number of interpretations with vs. without AI for each radiologist, allowing for quantification of the radiologist-level AI learning curve. We are not randomizing at the facility level as some radiologists interpret exams acquired at different facilities on the same day. By randomizing AI at the exam level, we will have the best ability to estimate and adjust for temporal trends in screening outcomes across individual radiologists. Randomization across large regional health systems will be managed independently at each participating site.
Our RCT randomizes screening mammography exams to be interpreted either with or without an AI decision-support tool. As a result, radiologists cannot be blinded to study arm during screening mammography interpretation. However, interpreting radiologists and facility staff (e.g., those scheduling the exams) will not know in advance which patients will be randomized to the AI tool. Randomization occurs within minutes after the breast imaging acquisition (i.e., when the mammography technologist captures the images) by an automated system that was developed by a third-party AI platform and successfully piloted at UCLA. Thus, the AI data (or lack thereof) is embedded within the mammogram before the radiologist opens the exam, preventing any option to "add AI" to an exam randomized to be interpreted without AI. Radiologists will be aware of AI availability only at the time of interpretation, as AI information will appear upon opening the exam (e.g., the AI information pops up with the exam images).
Eligibility criteria
This trial will include all radiologists interpreting screening mammography and all adult patients undergoing screening mammography at any of the participating breast imaging facilities across 6 regional health systems (UCLA, UC San Diego, University of Washington-Seattle, University of Wisconsin-Madison, Boston Medical Center, and University of Miami) during the trial period. Individuals must meet the following eligiblity criteria.
Inclusion Criteria:
1. Be at least 18 years of age or older
2. Receive a screening mammogram at one of the participating breast imaging facilities OR be a radiologist who interprets screening mammograms at one of the participating breast imaging facilities.
Exclusion Criteria:
1\. Patients who have opted out of all research at the health system
Study design
Enrollment target: 400000 participants
Allocation: randomized
Masking: single
Age groups: adult, older_adult
Timeline
Starts: 2025-10-15
Estimated completion: 2030-03-01
Last updated: 2025-11-26
Interventions
Device: Artificial intelligence (AI) decision-support tool
Primary outcomes
- • Cancer detection rate (Cancer diagnosed within 90 days of positive study entry screening mammogram)
- • Recall rate (Through study completion, an average of 1 year)
Sponsor
Jonsson Comprehensive Cancer Center · other
With: University of California, Los Angeles, University of California, San Diego, University of Wisconsin, Madison, Boston Medical Center, Patient-Centered Outcomes Research Institute, University of Washington, California Breast Cancer Research Program, University of Miami, University of California, Davis
Contacts & investigators
ContactMichelle L'Hommedieu, PhD · contact · mlhommedieu@mednet.ucla.edu · (310) 592-9454
InvestigatorJoann G Elmore, MD, MPH · principal_investigator, University of California, Los Angeles
InvestigatorDiana Miglioretti, PhD · principal_investigator, University of California, Davis
All locations (6)
University of California Los Angeles Health SystemRecruiting
Los Angeles, California, United States
University of California, San DiegoRecruiting
San Diego, California, United States
University of Miami Health SystemRecruiting
Miami, Florida, United States
Boston Medical CenterRecruiting
Boston, Massachusetts, United States
University of Washington Health SystemRecruiting
Seattle, Washington, United States
University of Wisconsin-MadisonRecruiting
Madison, Wisconsin, United States