인공 지능이 유방암 검진에 미치는 영향
인공 지능이 유방암 선별 질 및 효과에 미치는 영향 평가 : 전향 적 무작위 대조 시험
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.
연구 개요
상태
상태
정황
정황
개입 / 치료
개입 / 치료
상세 설명
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).
연구 유형
연구 유형
등록 (추정된)
등록
단계
단계
- 4단계
연락처 및 위치
연구 연락처
연구 연락처
- 이름: Michelle L'Hommedieu, PhD
- 전화번호: (310) 592-9454
- 이메일: mlhommedieu@mednet.ucla.edu
연구 장소
-
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California
-
Los Angeles, California, 미국, 90024
- 모병
- University of California Los Angeles Health System
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수석 연구원:
- Hannah S. Milch, MD
-
연락하다:
- Michelle L'Hommedieu, PhD
- 전화번호: (310) 592-9454
- 이메일: mlhommedieu@mednet.ucla.edu
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수석 연구원:
- Joann G Elmore, MD, MPH
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San Diego, California, 미국, 92093
- 모병
- University of California, San Diego
-
수석 연구원:
- Haydee Ojeda-Fournier, MD
-
연락하다:
- Haydee Ojeda-Fournier, MD
- 전화번호: (858) 442-1902
- 이메일: hojeda@health.ucsd.edu
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Florida
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Miami, Florida, 미국, 33136
- 모병
- University of Miami Health System
-
연락하다:
- Jose Net, MD
- 전화번호: (305) 215-0461
- 이메일: jnet@med.miami.edu
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수석 연구원:
- Jose Net, MD
-
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Massachusetts
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Boston, Massachusetts, 미국, 02118
- 모병
- Boston Medical Center
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연락하다:
- Clare Poynton, MD, PhD
- 전화번호: (617) 638-6626
- 이메일: clare.poynton@bmc.org
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수석 연구원:
- Clare Poynton, MD, PhD
-
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Washington
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Seattle, Washington, 미국, 98195
- 모병
- University of Washington Health System
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연락하다:
- Janie Lee, MD, MSc
- 전화번호: (206) 606-6241
- 이메일: jmlee58@fredhutch.org
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수석 연구원:
- Janie Lee, MD, MSc
-
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Wisconsin
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Madison, Wisconsin, 미국, 53706
- 모병
- University of Wisconsin-Madison
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연락하다:
- Christoph Lee, MD, MSc
- 전화번호: (608) 263-9377
- 이메일: cilee3@uwhealth.org
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수석 연구원:
- Christoph Lee, MD, MSc
-
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참여기준
자격 기준
자격 기준
공부할 수 있는 나이
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
설명
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:
- Be at least 18 years of age or older
- 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
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 상영
- 할당: 무작위
- 중재 모델: 병렬 할당
- 마스킹: 하나의
팔의 수
무기와 개입
참가자 그룹 / 팔참가자 그룹 / 팔 |
개입 / 치료개입 / 치료 |
|---|---|
|
활성 비교기: Intervention (radiologist assisted by AI)
3D screening exams randomized to this arm will be interpreted by the radiologist assisted by the AI decision-support tool (i.e., intervention). |
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. |
|
간섭 없음: Standard care (radiologist alone)
3D screening exams randomized to this arm will be interpreted in accordance with standard care (i.e., interpreted by the radiologist alone, without an AI decision-support tool's assistance). |
연구는 무엇을 측정합니까?
주요 결과 측정
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Cancer detection rate
기간: Cancer diagnosed within 90 days of positive study entry screening mammogram
|
Number of screening exams recommended for breast biopsy (final Breast Imaging- Reporting and Data System [BI-RADS] assessment of 4 or 5) resulting in detected cancer, per 1,000 screening exams |
Cancer diagnosed within 90 days of positive study entry screening mammogram
|
|
Recall rate
기간: Through study completion, an average of 1 year
|
Number of screening exams recalled for diagnostic work-up (initial BI-RADS assessment of 0, 3, 4, or 5), per 1,000 screening exams |
Through study completion, an average of 1 year
|
2차 결과 측정
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Interval cancer rate (i.e., false-negative rate)
기간: Cancer diagnosed within 365 days of a negative study entry screening mammogram
|
Number of screening exams with a negative assessment (final BI-RADS assessment of 1 or 2) and breast cancer diagnosed within 1 year, per 1,000 screening exams |
Cancer diagnosed within 365 days of a negative study entry screening mammogram
|
|
False positive recall rate
기간: No cancer diagnosed within 365 days of a positive study entry screening mammogram
|
Proportion of screening exams recalled for additional imaging (final BI-RADS assessment of 1, 2, or 3), with no breast cancer diagnosed within 1 year |
No cancer diagnosed within 365 days of a positive study entry screening mammogram
|
|
False positive short-interval follow-up recommendation rate
기간: No cancer diagnosed within 365 days of a positive study entry screening mammogram
|
Proportion of screening exams recalled for short-interval follow-up (final BI-RADS assessment of 3) with no breast cancer diagnosed within 1 year |
No cancer diagnosed within 365 days of a positive study entry screening mammogram
|
|
False positive biopsy recommendation rate
기간: No cancer diagnosed within 365 days of a positive study entry screening mammogram
|
Proportion of screening exams recalled for breast biopsy (final BI-RADS assessment of 4 or 5) with no breast cancer diagnosed within 1 year |
No cancer diagnosed within 365 days of a positive study entry screening mammogram
|
|
Trust and confidence in AI
기간: Years 1,2 and Years 4,5
|
Trust and confidence in AI gathered from focus group and survey data |
Years 1,2 and Years 4,5
|
|
Efficiency metrics (only for the UCLA site)
기간: Through study completion, an average of 1 year
|
Interpretation time required for radiologists to interpret each mammogram with versus without AI. Delivery time, using time stamp data from exam acquisition to delivery of results to patients (aka turnaround time). |
Through study completion, an average of 1 year
|
공동 작업자 및 조사자
협력자
협력자
수사관
수사관
- 수석 연구원: Diana Miglioretti, PhD, University of California, Davis
- 수석 연구원: Joann G Elmore, MD, MPH, University of California, Los Angeles
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
연구 시작
기본 완료 (추정된)
기본 완료
연구 완료 (추정된)
연구 완료
연구 등록 날짜
최초 제출
최초 제출
QC 기준을 충족하는 최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
처음 게시됨
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
마지막 업데이트 게시됨
QC 기준을 충족하는 마지막 업데이트 제출
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
기타 연구 ID 번호
- 24-1192
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
A de-identified dataset from this study will be deposited in the Patient-Centered Outcomes Data Repository (PCODR) housed at the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan, in compliance with PCORI's Policy on Data Management and Data Sharing.
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
미국에서 제조되어 미국에서 수출되는 제품
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