Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation

人工智能提高乳腺癌筛查准确性：外部验证、细化和临床转化

• 批准号: 10544496
• 负责人: CHRISTOPH I LEE
• 金额: $ 51.77万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10544496
• 关键词: 3-Dimensional; Address; Algorithms; Artificial Intelligence; Benign; Big Data; Biometry; Biopsy; Breast; Breast Cancer Detection; California; Characteristics; Clinical; Code; Collaborations; Community Practice; Computer software; Computers; DNA Sequence Alteration; Data; Data Science; Data Set; Data Storage and Retrieval; Databases; Diagnosis; Diagnostic; Digital Breast Tomosynthesis; Digital Mammography; Equilibrium; Goals; Grant; Health; Human; Image; Imaging Device; Imaging technology; Institution; International; Lead; Link; Machine Learning; Malignant Neoplasms; Mammographic screening; Mammography; Medicine; Metadata; Methods; Modality; Modeling; Molecular; Nature; Oncology; Outcome; Output; Participant; Pathology; Performance; Population; Positioning Attribute; Publications; Radiology Specialty; Randomized, Controlled Trials; Registries; Reverse engineering; Risk; Seminal; Series; Supervision; Techniques; Technology Assessment; Testing; Time; Training; Translating; Universities; Validation; Washington; Woman; algorithm development; algorithmic methodologies; artificial intelligence algorithm; augmented intelligence; breast imaging; clinical implementation; clinical risk; clinical translation; clinically relevant; clinically significant; cloud based; computer aided detection; crowdsourcing; deep learning; digital; experience; genetic risk factor; improved; industry partner; innovation; long short term memory; malignant breast neoplasm; molecular subtypes; mortality; multidisciplinary; novel; novel strategies; patient population; population based; prospective; radiologist; routine screening; screening; tool; tumor

PROJECT SUMMARY Screening mammography saves lives but human interpretation alone is imperfect and is associated with significant harms including ~30,000 missed breast cancers and ~3.8 million false-positives exams each year in the U.S. alone. Traditional computer-aided detection failed to improve screening accuracy, in part due to the static nature of software trained and tested on small datasets decades ago. Recent advances in improved computer processing power, cloud-based data storage capabilities, and availability of large imaging datasets have led to renewed excitement for applying artificial intelligence (AI) to mammography interpretation. We propose a unique academic-industry partnership to validate, refine, scale, and clinically translate our proven 2D mammography AI algorithm to 3D mammography interpretation. Our team helped organize and lead the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Digital Mammography Challenge, an open crowdsourced AI algorithmic challenge that provided >640,000 digital 2D mammogram images and associated clinical metadata to >1,200 coding teams worldwide. Our industry partner for this grant, DeepHealth, Inc., was the top performing team in the DREAM Challenge. With >50% of U.S. facilities now offering 3D mammography for screening, the 50-to-100-fold increase in imaging data represents a new critical barrier for both radiologists and AI algorithm developers. To date, there have been few publications addressing AI-enhanced interpretation of 3D mammography, the emerging screening exam of choice. We will validate our post-DREAM algorithm for 2D mammography (which currently rivals human interpretation alone) using UCLA's Athena Breast Health Network, one of the largest population-based breast imaging registries. We will enhance our 2D AI algorithm with expert radiologist supervision and examine the impact of adding novel non-imaging data parameters, including genetic mutation and tumor molecular subtype data, to help train the AI model to identify more clinically significant cancers. We will use several novel technical algorithmic approaches to scale from 2D to 3D mammography which, in our preliminary studies, have shown improved accuracy beyond radiologist interpretation alone. Finally, we will perform a series of interpretive studies to identify the optimal interface between “black box” outputs and radiologist interpreters, which remains an understudied topic. With >40 million U.S. women undergoing screening each year, seemingly small improvements in overall accuracy would still imply significantly improved population-based outcomes. In summary, we have assembled an unparalleled multidisciplinary team with expertise in machine/deep learning, breast cancer screening accuracy, medicine, oncology, radiology, imaging technology assessment, and biostatistics. We have a proven track record of strong collaboration and are well positioned to validate, enhance, scale, and translate our proven 2D AI algorithm for improved 3D mammography accuracy. Our new end user tool will help tip the balance of routine screening towards greater benefits than harms.

项目摘要 筛查乳房X光检查可以挽救生命，但仅凭人类的解释是不完美的， 重大危害，包括每年约30，000例乳腺癌漏诊和约380万例假阳性检查 仅在美国。传统的计算机辅助检测未能提高筛查精度，部分原因是 几十年前在小数据集上训练和测试的软件的静态性质。改进的最新进展 计算机处理能力、基于云的数据存储能力和大型成像数据集的可用性 将人工智能（AI）应用于乳房X光检查解释，引发了新的兴奋。 我们提出了一个独特的学术-行业合作伙伴关系，以验证，完善，规模和临床转化我们的 经过验证的2D乳腺X线摄影AI算法到3D乳腺X线摄影解释。我们的团队帮助组织和 领导逆向工程评估和方法对话（DREAM）数字乳腺X射线摄影 Challenge，一个开放的众包AI算法挑战赛，提供了超过640，000张数字2D乳房X光照片 图像和相关的临床元数据提供给全球超过1，200个编码团队。我们的行业合作伙伴 grant，DeepHealth，Inc.，是梦幻挑战赛中表现最好的队伍拥有超过50%的美国工厂 现在提供3D乳房X光检查筛查，成像数据增加了50到100倍，这代表了一种新的 放射科医生和人工智能算法开发人员的关键障碍。迄今为止， 解决人工智能增强的3D乳腺X射线摄影解释，新兴的筛选检查的选择。 我们将验证我们的后梦想算法的2D乳腺X射线摄影（目前竞争对手的人类解释 单独）使用加州大学洛杉矶分校的雅典娜乳房健康网络，最大的人口为基础的乳房成像之一 登记处。我们将在放射科专家的监督下增强我们的2D AI算法，并检查 增加新的非成像数据参数，包括基因突变和肿瘤分子亚型数据， 帮助训练人工智能模型，以识别更多临床上重要的癌症。我们将使用几种新的技术 从2D到3D乳腺X射线摄影的算法方法，在我们的初步研究中， 提高了准确性，超出了放射科医生的解释。最后，我们将执行一系列解释性的 研究旨在确定“黑匣子”输出和放射科医生口译员之间的最佳接口， 仍然是一个未充分研究的话题。每年有超过4000万美国女性接受筛查， 总体准确性的微小改进仍意味着基于人口的结果得到显著改善。 总之，我们组建了一支无与伦比的多学科团队，拥有机器/深加工方面的专业知识， 学习，乳腺癌筛查准确性，医学，肿瘤学，放射学，成像技术评估， 和生物统计学。我们有着良好的合作记录，并有能力验证， 增强、缩放和转换我们经过验证的2D AI算法，以提高3D乳腺X射线摄影的准确性。我们的新 最终用户工具将有助于平衡常规筛查，使其利大于弊。