Department of Orthopaedics and Rehabilitation, Division of Musculoskeletal Sciences, College of Medicine, The Pennsylvania State University, 30 Hope Drive, EC089, Hershey, PA 17033, USA.
Osteoarthritis Cartilage. 2013 Oct;21(10):1550-7. doi: 10.1016/j.joca.2013.06.007. Epub 2013 Jun 15.
There is an interest in using Magnetic Resonance Imaging (MRI) to identify pre-radiographic changes in osteoarthritis (OA) and features that indicate risk for disease progression. The purpose of this study is to identify image features derived from MRI T2 maps that can accurately predict onset of OA symptoms in subjects at risk for incident knee OA.
Patients were selected from the Osteoarthritis Initiative (OAI) control cohort and incidence cohort and stratified based on the change in total Western Ontario and McMaster Universities Arthritis (WOMAC) score from baseline to 3-year follow-up (80 non-OA progression and 88 symptomatic OA progression patients). For each patient, a series of image texture features were measured from the baseline cartilage T2 map. A linear discriminant function and feature reduction method was then trained to quantify a texture metric, the T2 texture index of cartilage (TIC), based on 22 image features, to identify a composite marker of T2 heterogeneity.
Statistically significant differences were seen in the baseline T2 TIC between the non-progression and symptomatic OA progression populations. The baseline T2 TIC differentiates subjects that develop worsening of their WOMAC score OA with an accuracy between 71% and 76%. The T2 TIC differences were predominantly localized to a dominant knee compartment that correlated with the mechanical axis of the knee.
Baseline heterogeneity in cartilage T2 as measured with the T2 TIC index is able to differentiate and predict individuals that will develop worsening of their WOMAC score at 3-year follow-up.
人们有兴趣使用磁共振成像（MRI）来识别骨关节炎（OA）的放射前变化和预示疾病进展的特征。本研究的目的是确定从 MRI T2 图谱中得出的图像特征，这些特征可以准确预测处于膝关节 OA 发病风险的受试者出现 OA 症状。
从 Osteoarthritis Initiative（OAI）对照组和发病队列中选择患者，并根据基线至 3 年随访时总 Western Ontario 和 McMaster 大学关节炎（WOMAC）评分的变化进行分层（80 例非 OA 进展和 88 例症状性 OA 进展患者）。对于每个患者，从基线软骨 T2 图谱中测量了一系列图像纹理特征。然后，使用线性判别函数和特征减少方法来量化基于 22 个图像特征的纹理度量，即软骨 T2 纹理指数（TIC），以识别 T2 异质性的综合标志物。
在非进展和症状性 OA 进展人群之间，基线 T2 TIC 存在统计学差异。基线 T2 TIC 可区分 WOMAC 评分恶化的受试者，准确率在 71%至 76%之间。T2 TIC 差异主要局限于一个主导的膝关节腔，与膝关节的机械轴相关。
用 T2 TIC 指数测量的软骨 T2 基线异质性能够区分和预测在 3 年随访时 WOMAC 评分恶化的个体。
