Deep Learning-based Imaging Biomarkers for Knee Osteoarthritis

基于深度学习的膝骨关节炎成像生物标志物

• 批准号: 10395927
• 负责人: Cem Murat Deniz
• 金额: $ 49.21万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-02 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10395927
• 关键词: 3-Dimensional; Address; Algorithms; Bilateral; Biological Markers; Case Study; Chronic; Clinical; Clinical Data; Clinical Trials; Cohort Studies; Collaborations; Complex; Data; Data Set; Degenerative Disorder; Degenerative polyarthritis; Development; Diagnostic radiologic examination; Direct Costs; Disease; Disease Progression; Goals; Health; Image; Image Analysis; Individual; Intervention; Intervention Studies; Joints; Knee; Knee Osteoarthritis; Knee joint; Knowledge; Lead; Length; Location; Magnetic Resonance; Magnetic Resonance Imaging; Measurable; Medical Imaging; Methods; Minor; Modeling; Monitor; Musculoskeletal System; Outcome; Participant; Patient-Focused Outcomes; Patients; Physicians; Play; Probability; Prognosis; Replacement Arthroplasty; Research; Resources; Risk; Risk Factors; Role; Severities; Statistical Data Interpretation; System; Techniques; Therapeutic Intervention; Training; Visit; arthropathies; automated algorithm; automated analysis; base; biomarker identification; bone; clinical practice; clinical risk; cohort; convolutional neural network; deep learning; deep learning algorithm; deep learning model; effective therapy; feature extraction; high risk; imaging biomarker; improved; in vivo; information model; innovation; insight; learning strategy; novel; outcome prediction; patient stratification; predictive marker; predictive modeling; prevent; prognostic model; recurrent neural network; risk prediction; tool

ABSTRACT In the U.S., more than 600,000 knee osteoarthritis (OA)-related total knee joint replacement (TKR) cases are reported every year, exceeding $17 billion estimated direct costs annually. There is a growing need for disease- modifying therapies that prevent or delay the need for TKR. However, development of such therapies remains challenging due to the lack of objective and measurable OA biomarkers for disease progression. The course of the OA is highly variable between individuals and the OA progresses too slowly, making it difficult to identify sensitive OA biomarkers capable of capturing minor changes on the knee joint. This has slowed development of effective therapies and prevents physicians from providing the most effective advice about minimizing the need for TKR. In this project, our goal is to develop imaging biomarkers to monitor minor OA-related changes in knee joint health that lead to TKR. To achieve this goal, we will combine novel deep learning algorithms with clinical and imaging data from the Osteoarthritis Initiative (OAI). The OAI dataset includes clinical data, biospecimens, radiographs, and magnetic resonance (MR) images collected over 8 years. The proposed project has three Specific Aims: (i) to develop an automated OA-relevant biomarker identification tool from the bilateral posteroanterior fixed-flexion knee radiographs using deep convolutional neural networks (CNNs) and recurrent neural networks (RNNs) combined with the OA progression outcome of subjects (n = 882); (ii) to develop an automated OA-relevant biomarker identification tool from structural and compositional MR images using 3D CNNs with RNNs combined with the OA progression outcome of subjects (n = 882); and (iii) to determine whether deep learning–based imaging biomarkers can act as surrogates to predict the OA progression using a subject cohort (n = 296) independent of the cohort used to identify imaging biomarkers. The proposed project will couple deep learning with diagnostic radiology to unveil key combinations of OA-relevant features directly from images with minimal user interaction. This will facilitate fast individualized assessment of OA progression using whole knee joint images directly. If successful, this study will bring new insights into the development of imaging biomarkers for OA progression and more broadly into our understanding and treatment of OA. The knowledge gained in this project will help to advance close monitoring of OA progression by opening new perspectives on the regions and parameters for potential inclusion in both intervention studies and clinical practice.

摘要 在美国，超过600，000例膝关节骨关节炎（OA）相关的全膝关节置换（TKR）病例 每年报告的直接费用估计超过170亿美元。对疾病的需求越来越大- 修改预防或延迟TKR需求的治疗方法。然而，这种疗法的发展仍然存在。 由于缺乏疾病进展的客观和可测量的OA生物标志物，因此具有挑战性。过程 OA在个体之间具有高度可变性，并且OA进展太慢，使得难以识别 能够捕获膝关节微小变化的敏感OA生物标志物。这减缓了发展， 有效的治疗，并阻止医生提供最有效的建议，尽量减少需要 对于TKR。在这个项目中，我们的目标是开发成像生物标志物来监测膝关节OA相关的微小变化。 导致TKR关节健康。为了实现这一目标，我们将联合收割机结合新型深度学习算法和临床 和来自骨关节炎倡议（OAI）的成像数据。OAI数据集包括临床数据、生物样本、 X光片和磁共振（MR）图像收集了8年。该项目有三个 具体目标：（i）开发一种自动化的OA相关生物标志物识别工具， 使用深卷积神经网络（CNN）和复发性后前位固定屈曲膝关节X线片 神经网络（RNN）与受试者（n = 882）的OA进展结果相结合;（ii）开发一个 使用3D从结构和成分MR图像自动识别OA相关生物标志物的工具 CNN与RNN结合受试者的OA进展结果（n = 882）;以及（iii）确定是否 基于深度学习的成像生物标志物可以作为替代物，使用受试者预测OA进展 队列（n = 296），独立于用于鉴定成像生物标志物的队列。该项目将结合 通过诊断放射学进行深度学习，直接从图像中揭示OA相关特征的关键组合 最小化用户交互。这将有助于使用整体评估快速个性化评估OA进展。 膝关节直接成像。如果成功，这项研究将为成像技术的发展带来新的见解 OA进展的生物标志物，更广泛地涉及我们对OA的理解和治疗。知识 在本项目中获得的成果将有助于通过开辟新的视角来促进对OA进展的密切监测， 可能纳入干预研究和临床实践的区域和参数。