乳がんスクリーニングに対する人工知能の影響
乳がんのスクリーニングの質と有効性に対する人工知能の影響の評価:前向きランダム化比較試験
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.
調査の概要
状態
状態
条件
条件
介入・治療
介入・治療
詳細な説明
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).
研究の種類
研究の種類
入学 (推定)
入学
段階
段階
- フェーズ 4
連絡先と場所
研究連絡先
研究連絡先
- 名前:Michelle L'Hommedieu, PhD
- 電話番号:(310) 592-9454
- メール:mlhommedieu@mednet.ucla.edu
研究場所
-
-
California
-
Los Angeles、California、アメリカ、90024
- 募集
- University of California Los Angeles Health System
-
主任研究者:
- Hannah S. Milch, MD
-
コンタクト:
- Michelle L'Hommedieu, PhD
- 電話番号:(310) 592-9454
- メール:mlhommedieu@mednet.ucla.edu
-
主任研究者:
- Joann G Elmore, MD, MPH
-
San Diego、California、アメリカ、92093
- 募集
- University of California, San Diego
-
主任研究者:
- Haydee Ojeda-Fournier, MD
-
コンタクト:
- Haydee Ojeda-Fournier, MD
- 電話番号:(858) 442-1902
- メール:hojeda@health.ucsd.edu
-
-
Florida
-
Miami、Florida、アメリカ、33136
- 募集
- University of Miami Health System
-
コンタクト:
- Jose Net, MD
- 電話番号:(305) 215-0461
- メール:jnet@med.miami.edu
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主任研究者:
- Jose Net, MD
-
-
Massachusetts
-
Boston、Massachusetts、アメリカ、02118
- 募集
- Boston Medical Center
-
コンタクト:
- Clare Poynton, MD, PhD
- 電話番号:(617) 638-6626
- メール:clare.poynton@bmc.org
-
主任研究者:
- Clare Poynton, MD, PhD
-
-
Washington
-
Seattle、Washington、アメリカ、98195
- 募集
- University of Washington Health System
-
コンタクト:
- Janie Lee, MD, MSc
- 電話番号:(206) 606-6241
- メール:jmlee58@fredhutch.org
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主任研究者:
- Janie Lee, MD, MSc
-
-
Wisconsin
-
Madison、Wisconsin、アメリカ、53706
- 募集
- University of Wisconsin-Madison
-
コンタクト:
- Christoph Lee, MD, MSc
- 電話番号:(608) 263-9377
- メール:cilee3@uwhealth.org
-
主任研究者:
- Christoph Lee, MD, MSc
-
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参加基準
適格基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
説明
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
研究計画
研究はどのように設計されていますか?
デザインの詳細
- 主な目的:ふるい分け
- 割り当て:ランダム化
- 介入モデル:並列代入
- マスキング:独身
アーム数
武器と介入
参加者グループ / アーム参加者グループ / アーム |
介入・治療介入・治療 |
|---|---|
|
アクティブコンパレータ: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. |
|
介入なし: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). |
この研究は何を測定していますか?
主要な結果の測定
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Cancer detection rate
時間枠: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
時間枠: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
|
二次結果の測定
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Interval cancer rate (i.e., false-negative rate)
時間枠: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
時間枠: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
時間枠: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
時間枠: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
時間枠: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)
時間枠: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
|
協力者と研究者
協力者
協力者
捜査官
捜査官
- 主任研究者:Diana Miglioretti, PhD、University of California, Davis
- 主任研究者:Joann G Elmore, MD, MPH、University of California, Los Angeles
研究記録日
主要日程の研究
研究開始 (実際)
研究開始
一次修了 (推定)
一次修了
研究の完了 (推定)
研究の完了
試験登録日
最初に提出
最初に提出
QC基準を満たした最初の提出物
QC基準を満たした最初の提出物
最初の投稿 (実際)
最初の投稿
学習記録の更新
投稿された最後の更新 (実際)
投稿された最後の更新
QC基準を満たした最後の更新が送信されました
QC基準を満たした最後の更新が送信されました
最終確認日
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
その他の研究ID番号
- 24-1192
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
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.
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
米国で製造され、米国から輸出された製品。
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