Clinical trial NCT06934239: Impact of Artificial Intelligence on Breast Cancer Screening
Official title: Evaluating the Impact of Artificial Intelligence on Breast Cancer Screening Quality and Effectiveness: A Prospective Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Breast Cancer Screening
Artificial Intelligence (AI)
Keywords: Breast cancer screening
Artificial intelligence (AI)
Lead sponsor: Jonsson Comprehensive Cancer Center
Sponsor class: OTHER
Collaborators: name: University of California, Los Angeles
name: National Institutes of Health (NIH)
name: National Cancer Institute (NCI)
First posted: 2025-04-18
Enrollment: 154474
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: This is a study of an FDA-cleared artificial intelligence (AI) decision-support tool.
Primary purpose: SCREENING

Brief summary: The goal of this clinical trial is to compare patient-centered outcomes when 3D screening mammograms are interpreted with versus without a leading FDA-cleared AI decision-support tool in real-world U.S. settings and to assess patient's perspectives on AI in medicine.

The main questions it aims to answer are:

1. Will AI use be associated with an increase in cancer detection and an initially higher recall rate as radiologists start using AI, followed by a recall rate comparable to that without AI (no more than 1.5 percentage-points higher) after a learning curve period? Will AI use will be associated with lower rates of missed breast cancers and similar rates of false alarms after a learning curve period?
2. Will improved patient outcomes with AI be most pronounced for exams on women who are White, older, and have less dense breasts, and on baseline exams? Will AI aid patient outcomes when the interpretation is by radiologists with less clinical experience, lower annual interpretive volume, and less tolerance of ambiguity? Yet, will there be greater automation bias (the tendency for humans to defer to a computer algorithms' results) noted among these radiologists?
3. What are patients' perspectives on AI in mammography, including their confidence in breast cancer screening when interpreted with vs. without AI? What are patients' perspectives on the importance of the study results?

Researchers will compare patient-centered outcomes when 3D screening mammograms are interpreted with versus without a leading FDA-cleared AI decision-support tool in real-world U.S. settings.

This trial will include all adult patients undergoing 3D mammography breast cancer screening at imaging facilities across University of California at Los Angeles and University of Washington health systems and all radiologists interpreting breast cancer screening. All screening mammograms at these facilities will be randomized to either intervention (radiologist with AI support) versus usual care (radiologist alone) to see if interpreting these mammograms with the AI tool's assistance improves patient outcomes.

Detailed description: AIM 1. Conduct a 2-year randomized controlled trial (RCT) and assess immediate performance measures and outcomes of the diagnostic evaluation cascade for 3D screening mammograms interpreted with vs. without AI: Outcomes will include recall rate, cancer detection rate, and positive predictive value (PPV1), along with measures of the diagnostic evaluation cascade including short-interval follow-up rate, biopsy rate, and biopsy yield (i.e., PPV2 and PPV3).

AIM 2: Assess 1-year breast cancer performance measures and clinical patient outcomes for 3D screening mammograms interpreted with vs. without AI: After linkage with state and regional tumor registries, we will compare longer-term performance measures (sensitivity, specificity, false positive rates) and patient outcomes (interval cancer rates and tumor molecular subtypes) associated with screening with vs. without AI.

AIM 3: We will perform subgroup analyses of the interaction of patient-, exam-, and radiologist-level characteristics associated with improved screening performance with AI. Patient characteristics will include demographics (e.g., age, race/ethnicity) and risk factors (e.g., breast density, family history of breast cancer, previous breast biopsy, prior breast cancer). Exam-level factors will include baseline vs. subsequent screening exams and mammography unit manufacturer. Radiologist characteristics will include experience level (e.g., general clinical experience and experience with AI), tolerance of ambiguity in clinical decision-making and trust in AI, measured with standardized survey tools.

Eligibility criteria: Inclusion Criteria:

- Be an adult patient undergoing 3D mammography breast cancer screening at any of the 16 imaging facilities across UCLA and UW health systems

OR

- Be a radiologist interpreting breast cancer screening at one of these imaging facilities

Exclusion Criteria:

- Is neither an adult patient undergoing 3D mammography breast cancer screening at any of the 16 imaging facilities across UCLA and UW health systems

NOR

- Is a radiologist interpreting breast cancer screening at one of these imaging facilities

Primary outcomes:
measure: Cancer detection rate
timeFrame: Cancer diagnosed within 90 days of positive study entry screening mammogram
measure: Recall rate
timeFrame: Day of interpretation
Secondary outcomes:
measure: Missed breast cancer rate (i.e., false-negative rate)
timeFrame: Cancer diagnosed within 365 days of a negative study entry screening mammogram
measure: False alarm (FA) recall
timeFrame: No cancer diagnosed within 365 days of study entry screening mammogram
measure: False alarm (FA) short-interval follow-up (SIFU)
timeFrame: No cancer diagnosed within 365 days of study entry screening mammogram
measure: False-alarm (FA) biopsy recommendation
timeFrame: No cancer diagnosed within 365 days of study entry screening mammogram
measure: Trust and confidence in AI
timeFrame: Years 1, 2, and Years 4, 5
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: University of California Los Angeles Health System
state: California
city: Seattle
country: United States
facility: University of Washington Health System
state: Washington
Study officials:
affiliation: University of California, Los Angeles
name: Hannah S Milch, MD
role: PRINCIPAL_INVESTIGATOR
References: