Population-Based Evaluation of Artificial Intelligence for Mammography Prior to Widespread Clinical Translation

在广泛临床转化之前对乳腺 X 线摄影人工智能进行基于人群的评估

• 批准号: 10651842
• 负责人: CHRISTOPH I LEE
• 金额: $ 61.29万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10651842
• 关键词: 3-Dimensional; Academy; Address; Adopted; Algorithms; Artificial Intelligence; Biometry; Breast Cancer Detection; Breast Cancer Epidemiology; Breast Cancer Surveillance Consortium; Cancer Detection; Cancer Intervention and Surveillance Modeling Network; Characteristics; Clinical; Clinical effectiveness; Clinical/Radiologic; Data; Data Set; Detection; Digital Breast Tomosynthesis; Digital Mammography; Effectiveness; Ensure; Evaluation; Feedback; Funding; Future; Geography; Health Benefit; Health Services Research; Healthcare; Human; Image; Industry; Informatics; Infrastructure; Institution; Internal Medicine; International; Knowledge; Label; Link; Malignant Neoplasms; Mammographic screening; Mammography; Medicine; Modeling; Outcome; Patient-Focused Outcomes; Performance; Physicians; Policy Maker; Population; Privatization; Prospective Studies; Reader; Registries; Screening procedure; Target Populations; Technology; Technology Assessment; Testing; Time; Translations; Triage; United States; United States Food and Drug Administration; Update; Validation; Visual Perception; Woman; Women' s Group; algorithm training; artificial intelligence algorithm; breast imaging; cancer diagnosis; clinical practice; clinical translation; cohort; comparative; computer aided detection; cost; cost effectiveness; deep learning algorithm; deep neural network; detection platform; digital technology; follow-up; improved; improved outcome; industry partner; malignant breast neoplasm; models and simulation; mortality; multilevel analysis; neoplasm registry; novel; population based; prospective; radiologist; screening; tool; tumor

PROJECT SUMMARY Multiple artificial intelligence (AI) technologies are now commercially available for automated interpretation of screening mammography. These AI technologies hold promise for improving screening performance and outcomes for the 40 million U.S. women who undergo routine breast cancer screening each year. Federal regulatory approval of new AI technologies requires only a demonstration of non-inferior accuracy to existing computer-aided detection systems in small, retrospective reader studies, but their widespread clinical translation is contingent upon more robust population-based evaluation. Specifically, the impact of these AI technologies on actual patient outcomes needs to be assessed, including whether or not they lead to improved detection of clinically meaningful cancers in the general screening population. Robust external validation of AI algorithms for mammography screening has thus far been limited by use of single institution datasets not representative of the entire target population, use of AI algorithms that are not publicly available, comparison to radiologist performance in enriched case sets, limited follow-up time for cancer diagnoses influencing ground truth labels, and evaluation on 2D digital mammography rather than 3D digital breast tomosynthesis (DBT) exams. Our study objective is to conduct a comparative evaluation of five commercially available AI technologies for automated DBT screening interpretation that overcomes all of these limitations and then estimate the long-term benefits, harms, and costs of AI-driven DBT screening at the U.S. population level. Specifically, we will 1) use a centralized honest broker, model-to-data paradigm infrastructure to perform an independent, external validation of five leading commercial AI technologies for DBT screening using prospectively collected data obtained from eight diverse U.S. regional breast imaging registries; 2) stratify AI vs. radiologist performance on detailed woman-, exam-, radiologist-, and tumor-level characteristics to inform targeted algorithm training and refinement efforts to ensure generalizability of the AI algorithms; 3) explore targeted approaches for improving clinical workflow efficiency by using AI to safely triage exams highly likely to be negative; and 4) use a validated breast cancer microsimulation model to determine population-level, long- term health benefits, harms, and costs associated with AI technologies for DBT screening both as a standalone screening tool and as a second independent reader to radiologist interpretation. Our proposed study will represent the most objective and rigorous evaluation of deep learning algorithms for DBT screening interpretation in the U.S. to date. Our results will provide urgently needed evidence to inform key stakeholders including women, physicians, payers, industry partners, and policymakers regarding how to maximize the value of AI technologies for DBT screening prior to their widespread clinical translation.

项目摘要 现在，多种人工智能（AI）技术可用于自动解释 筛查乳房X线摄影。这些AI技术有望改善筛查性能和 每年进行常规乳腺癌筛查的4000万美国妇女的结果。联邦 新AI技术的监管批准仅需要证明现有的非内部精度 小型回顾性读者研究中的计算机辅助检测系统，但它们的广泛临床 翻译取决于更强大的基于人群的评估。具体而言，这些AI的影响 需要评估有关实际患者结果的技术，包括它们是否导致改进 在一般筛查人群​​中检测临床意义的癌症。 AI的强大外部验证 到目前为止，乳房X线摄影筛查算法受到单个机构数据集的限制 代表整个目标人群的代表，使用未公开可用的AI算法，与 放射科医生在丰富的病例集中的表现，癌症诊断的随访时间有限 真实标签和对2D数字乳房摄影而不是3D数字乳房合成（DBT）的评估 考试。我们的研究目标是对五个市售AI进行比较评估 自动DBT筛选解释的技术，以克服所有这些限制，然后 估计在美国人口一级进行AI驱动的DBT筛查的长期收益，危害和成本。 具体来说，我们将1）使用集中的诚实经纪人，模型到数据范式基础架构来执行 独立的，外部验证五种领先的商业AI技术用于DBT筛选 从八个不同的美国区域乳房成像登记处获得的前瞻性收集数据； 2）分层AI 与详细女性，检查，放射科医生和肿瘤水平特征的vs.放射科医生的表现 有针对性的算法培训和完善工作，以确保AI算法的普遍性； 3）探索 通过使用AI安全的分类考试来提高临床工作流程效率的有针对性方法 负面； 4）使用经过验证的乳腺癌微仿真模型来确定种群级别的长期 与AI技术相关的DBT筛查的术语健康益处，危害和成本是独立的 筛选工具，作为放射科医生解释的第二个独立读者。我们提出的研究将 代表了DBT筛选的深度学习算法的最客观和严格的评估 迄今为止在美国的解释。我们的结果将提供急需的证据，以告知主要利益相关者 包括妇女，医生，付款人，行业合作伙伴和政策制定者，如何最大化 DBT筛查的AI技术的价值在其广泛的临床翻译之前。