A Trial Comparing Screening Mammography With and Without Assistance From Artificial Intelligence for Breast Cancer Detection and Recall Rates in Adult Patients

Conditions studied

Breast Cancer Screening, Artificial Intelligence (AI)

About this study

The goal of this clinical trial is to compare patient-centered outcomes when screening digital breast tomosynthesis (DBT) exams are interpreted with versus without a leading FDA-cleared artificial intelligence (AI) decision-support tool in real-world U.S. settings and to assess patients' and radiologists' perspectives on AI in medicine. The main question it aims to answer is: Does an FDA-cleared AI decision-support tool for digital tomosynthesis (DBT) improve screening outcomes in real world US clinical settings? This trial will include all interpreting radiologists and all adult patients undergoing screening mammography at any of the participating breast imaging facilities across 6 regional health systems (University of California, Los Angeles (UCLA), University of California, San Diego (UCSD), University of Washington-Seattle, University of Wisconsin-Madison, Boston Medical Center, and University of Miami) during the trial period. All screening mammograms at these facilities will be randomized to either intervention (radiologist assisted by an AI decision support tool) versus usual care (radiologist alone) to see if interpreting these mammograms with the AI tool's assistance improves patient screening outcomes. We are targeting 400,000 screening exams across the participating health systems in this trial.

Interventions

• Device: Artificial intelligence (AI) decision-support tool — The intervention is an AI decision-support tool to help radiologists interpret 3D screening mammograms. For exams randomized to this intervention arm, the first image displayed to the radiologist upon opening an exam on the viewing station will be a one-page, standardized AI report showing the overall exam risk (elevated, intermediate, or low), image region markings, lesion scores from 1-100 (100 being the highest suspicion), bounding boxes, and relevant slice locations for 3D exams. Radiologists can toggle markings on/off and retain full control over the final interpretation of the exam as positive or negative (i.e., they can choose to ignore the AI information). Randomization occurs 1:1 at the exam level via automated code at image acquisition. Returning patients in year two will be re-randomized. Radiologists cannot filter their exam lists by AI availability or risk, and randomization will be independently managed at each participating health system.

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)

Eligibility information

Study locations

• University of California Los Angeles Health System, Los Angeles, California 90024 United States
• University of California, San Diego, San Diego, California 92093 United States
• University of Miami Health System, Miami, Florida 33136 United States
• Boston Medical Center, Boston, Massachusetts 02118 United States
• University of Washington Health System, Seattle, Washington 98195 United States
• University of Wisconsin-Madison, Madison, Wisconsin 53706 United States