Using artificial intelligence to support efficient same-day diagnostic imaging in breast cancer screening

使用人工智能支持乳腺癌筛查中的高效当日诊断成像

• 批准号: 10582453
• 负责人: Anne C Hoyt
• 金额: $ 13.83万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2027-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10582453
• 关键词: Using; artificial; intelligence; support; efficient

Breast cancer is the most common solid cancer and the second leading cause of cancer death among U.S. women. Multiple studies have shown that screening mammography decreases breast cancer-related mortality, but the implementation of screening has inefficiencies and limitations that contribute to potential harms. False- positive results and wait times for further evaluation are well-documented mammographic harms. Approximately 10% of all screening exams are recalled for diagnostic workup, of which 95% are found to be false positives, potentially resulting in benign biopsies and overdiagnosis. The percentage of women recalled for further diagnostic workup varies between 8-14%, depending on the radiologist. Moreover, the anxiety experienced by a woman with a recent abnormal mammogram is significant. Many women sleep poorly and struggle to focus as they await a more definitive diagnostic workup. Given these limitations of screening, our overarching goal is to minimize practice variabilities associated with recalls, reduce patient anxiety, and increase patient satisfaction. We propose to assess the feasibility and effect of introducing artificial intelligence (AI) solution at the point of care to (1) reduce the overall callback rate, (2) increase patient satisfaction by providing immediate screening results, and for women who require further diagnostic workup, (3) eliminate the delay between screening and diagnostic workup. The AI solution enables immediate “online” interpretation of screening exams in a high- volume breast screening program. For women with abnormal mammograms, real-time interpretation of the screening exam permits women to be scheduled for a diagnostic exam on the same day. This goal is accomplished in three aims. Aim 1 will validate and integrate an AI algorithm to triage screening mammograms within our institution’s breast screening population. We will ensure that the algorithm performs at an expected level (i.e., non-inferior to existing radiologist performance) and integrate and refine the algorithm to communicate results clearly and efficiently to target users. Aim 2 will design and assess an AI-enabled workflow for same-day diagnostic exams. We will analyze the current state of care, identify impediments to implementing this program, and develop changes to the care pathway to allow an AI intervention. In Aim 3, we will implement and evaluate the impacts of an AI-enabled same-day diagnostic imaging paradigm in three stages: (1) a pilot stage, involving a subset of women undergoing screening using 2D screening mammography at a single site; (2) an implementation stage, involving a larger group of women undergoing 2D and 3D screening mammography at a single imaging center; and (3) an expansion stage, involving women being screened at a second imaging center. UCLA Health is a unique environment to evaluate this paradigm given the large number of screening exams performed annually (>40,000 exams) and the distributed nature of its breast screening program across twelve geographically separated imaging centers. The expected outcome of this project is a generalizable approach for evaluating and integrating AI algorithms to effect improvements in care delivery.

乳腺癌是美国最常见的实体癌，也是癌症死亡的第二大原因。 妇女多项研究表明，筛查性乳房X光检查可降低乳腺癌相关死亡率， 但是筛查的实施效率低下且存在局限性，这会导致潜在的危害。假的- 积极的结果和等待进一步评估的时间是有据可查的乳房X线摄影伤害。约 所有筛查检查中有10%被召回进行诊断检查，其中95%被发现是假阳性， 可能导致良性活检和过度诊断。再次召回的女性比例 根据放射科医师的不同，诊断检查在8- 14%之间变化。此外，一个人所经历的焦虑 最近有异常乳房X光检查的女性是很重要的。许多女性睡眠不好，难以集中注意力， 他们在等待更明确的诊断结果考虑到筛查的这些局限性，我们的总体目标是 最大限度地减少与召回相关的实践差异，减少患者焦虑，提高患者满意度。 我们建议评估引入人工智能解决方案的可行性和效果， 护理，以（1）降低总体回诊率，（2）通过提供即时筛查提高患者满意度 对于需要进一步诊断检查的妇女，（3）消除筛查和 诊断检查。人工智能解决方案能够在高质量的环境中即时“在线”解读筛查检查结果， 乳腺癌筛查计划。对于乳房X光检查异常的妇女， 筛查允许妇女在同一天安排诊断检查。这一目标 实现了三个目标。目标1将验证和集成AI算法来分类筛查乳房X线照片 在我们机构的乳房筛查人群中。我们将确保算法以预期的速度执行。 水平（即，不劣于现有放射科医师的表现），并整合和完善算法， 明确有效地向目标用户提供结果。Aim 2将设计和评估当天启用AI的工作流程 诊断检查。我们将分析护理的现状，确定实施该计划的障碍， 并对护理路径进行更改，以允许人工智能干预。在目标3中，我们将实施和评估 人工智能支持的同日诊断成像模式的影响分三个阶段：（1）试点阶段，包括 在单个部位使用2D筛查乳房X线摄影进行筛查的女性子集;（2） 实施阶段，涉及一个较大的妇女群体接受2D和3D筛查乳房X光检查， 单一成像中心;（3）扩展阶段，涉及在第二成像中心进行筛查的女性。 加州大学洛杉矶分校的健康是一个独特的环境，以评估这一范例鉴于大量的筛选考试 每年进行（> 40，000次检查），其乳房筛查计划的分布性质涉及12个国家， 地理上分散的成像中心。该项目的预期成果是一种可推广的方法， 评估和整合人工智能算法，以改善护理服务。