Identifying determinants of rapid structural and/or clinical progression in knee osteoarthritis by quantitative assessment of structural features on radiographs

骨关节炎是最常见的肌肉骨骼疾病，给社会带来很大的负担。膝 膝骨性关节炎患者的疼痛是导致身体残疾的主要原因，也是住院的主要原因 来访。膝骨性关节炎是一种疾病，这在一定程度上阻碍了人们对膝关节疼痛病因的认识。 全关节的多因素进行性疾病；因此，膝关节疼痛进行性可能是 随着时间的推移，几种不同结构特征的局部或区域性异常的结果。长期目标是 加快发展临床试验最佳入选筛选，检验前景 改善症状的治疗。本应用程序的总体目标是研究 不同膝关节疼痛测量(例如，膝关节疼痛)的时间模式的不同基于MRI的特征 频率和严重程度)。中心假设是有一些颞部膝部疼痛的表型 各种MRI定义的结构特征(例如，骨髓病变)与表型相关。 这一假说很大程度上是基于初步研究，包括骨关节炎倡议 (OAI)、多中心骨关节炎研究(MOST)、半定量(SQ)读数、复杂膝关节 OAI和大多数研究中的疼痛测量，以及关于机器/深度学习的项目以准确 预测OAI和MOST中没有现有放射科医生读数的磁共振成像的SQ读数 学习。中心假设将通过追求两个具体目标来检验：1)识别不同的颞膝 基于所有可用的膝关节纵向疼痛测量的疼痛表型和相关的膝关节疼痛风险 MOST和OAI中的因素；以及2)基线时MRI定义的结构特征与 确定了颞部膝部疼痛的表型。本申请中提出的研究在以下几个方面具有创新性 方式。它考虑了膝关节疼痛的各种定义和可用的疼痛测量数据 大型纵向OAI和大多数研究和应用机器学习、深度学习和统计方法 确定膝关节疼痛的表型，并将其与基于MRI的因素相关联。这一新的和实质性的 理解膝关节疼痛的不同方法有望克服现有研究的局限性 (例如，基于单膝疼痛测量和横断面研究)，从而为 检测不同的颞部膝关节疼痛表型，并识别高危人群 不同的颞膝疼痛表型，更有针对性地进入临床试验。

Osteoarthritis (OA) is the most common musculoskeletal disorder and presents a large societal burden. Knee pain in patients with knee OA is a leading contributor to physical disability and a major reason for hospital visits. An improved understanding of the etiology of knee pain has been hampered in part by knee OA being a multifactorial and progressive disease of the whole joint; consequently, knee pain progression may be the result of local or regional abnormalities of several different structural features over time. The long-term goal is to accelerate the development of optimal screening for enrollment into clinical trials to test promising treatments for symptom improvement. The overall objective in this application is to study the association of different MRI-based features with the temporal patterns of various knee pain measurements (e.g., knee pain frequency and severity) in OA. The central hypothesis is that there are some temporal knee pain phenotypes and various MRI-defined structural features (e.g., bone marrow lesions) are associated with the phenotypes. This hypothesis is formulated largely based on the preliminary studies, including the Osteoarthritis Initiative (OAI), the Multicenter Osteoarthritis Study (MOST), the semi-quantitative (SQ) readings, the complex knee pain measurements in the OAI and MOST studies, and projects on machine/deep learning to accurately predict SQ readings for MRIs that do not have existing radiologist-derived readings in the OAI and MOST studies. The central hypothesis will be tested by pursuing two specific aims: 1) identify different temporal knee pain phenotypes based on all available longitudinal knee pain measurements and the related knee pain risk factors in the MOST and OAI; and 2) associate the MRI-defined structural features at baseline with the identified temporal knee pain phenotypes. The research proposed in this application is innovative in several ways. It considers various definitions of knee pain and the available pain measurement data in the super- large longitudinal OAI and MOST studies and applies machine learning, deep learning and statistical methods to identify knee pain phenotypes and associate them with MRI-based factors. This new and substantively different approach to understanding knee pain is expected to overcome the limitations of existing studies (e.g., single knee pain measurement-based and cross-sectional studies), thereby opening new horizons for detecting different temporal knee pain phenotypes and allowing identification of individuals at high risk of various temporal knee pain phenotypes for more targeted enrollment into clinical trials.