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人工智能对乳腺癌筛查的影响

2026年11月5日 更新者：Jonsson Comprehensive Cancer Center

评估人工智能对乳腺癌筛查质量和有效性的影响：一项前瞻性随机对照试验

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.

研究概览

地位

招聘中

条件

干预/治疗

详细说明

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).

研究类型

介入性

注册 (估计的)

400000

阶段

  • 第四阶段

联系人和位置

本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。

学习联系方式

学习地点

  • 美国
    • California
      • Los Angeles、California、美国、90024
        • 招聘中
        • University of California Los Angeles Health System
        • 首席研究员：
          • Hannah S. Milch, MD
        • 接触：
        • 首席研究员：
          • Joann G Elmore, MD, MPH
      • San Diego、California、美国、92093
        • 招聘中
        • University of California, San Diego
        • 首席研究员：
          • Haydee Ojeda-Fournier, MD
        • 接触：
    • Florida
      • Miami、Florida、美国、33136
        • 招聘中
        • University of Miami Health System
        • 接触：
        • 首席研究员：
          • Jose Net, MD
    • Massachusetts
      • Boston、Massachusetts、美国、02118
        • 招聘中
        • Boston Medical Center
        • 接触：
        • 首席研究员：
          • Clare Poynton, MD, PhD
    • Washington
      • Seattle、Washington、美国、98195
        • 招聘中
        • University of Washington Health System
        • 接触：
        • 首席研究员：
          • Janie Lee, MD, MSc
    • Wisconsin
      • Madison、Wisconsin、美国、53706
        • 招聘中
        • University of Wisconsin-Madison
        • 接触：
        • 首席研究员：
          • Christoph Lee, MD, MSc

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

18年 及以上 (成人、年长者)

接受健康志愿者

是的

有资格学习的性别

全部

描述

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

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

  • 主要用途：放映
  • 分配：随机化
  • 介入模型：并行分配
  • 屏蔽：单身的

武器和干预

参与者组/臂
干预/治疗
有源比较器：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).
设备：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.
无干预：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).

研究衡量的是什么？

主要结果指标

结果测量
措施说明
大体时间
Cancer detection rate
大体时间：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
大体时间：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

次要结果测量

结果测量
措施说明
大体时间
Interval cancer rate (i.e., false-negative rate)
大体时间：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
大体时间：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
大体时间：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
大体时间：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
大体时间：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)
大体时间：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

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

Jonsson Comprehensive Cancer Center

合作者

Patient-Centered Outcomes Research Institute
University of California, San Diego
University of California, Los Angeles
University of Miami
University of Wisconsin, Madison
Boston Medical Center
University of California, Davis
University of Washington
California Breast Cancer Research Program

调查人员

  • 首席研究员：Diana Miglioretti, PhD、University of California, Davis
  • 首席研究员：Joann G Elmore, MD, MPH、University of California, Los Angeles

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2025年10月15日

初级完成 (估计的)

2028年3月1日

研究完成 (估计的)

2030年3月1日

研究注册日期

首次提交

2025年4月11日

首先提交符合 QC 标准的

2025年4月11日

首次发布 (实际的)

2025年4月18日

研究记录更新

最后更新发布 (实际的)

2026年11月9日

上次提交的符合 QC 标准的更新

2026年11月5日

最后验证

2026年11月1日

更多信息

与本研究相关的术语

关键字

其他研究编号

  • 24-1192

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

是的

IPD 计划说明

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.

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

是的

在美国制造并从美国出口的产品

不

此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.

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