Thaha Rafeek, Jogi Sandeep P, Rajan Sriram, Mahajan Vidur, Mehndiratta Amit, Singh Anup
Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
Department of Biomedical Engineering, ASET, Amity University, Gurgaon, Haryana, India.
J Orthop Res. 2022 Apr;40(4):779-790. doi: 10.1002/jor.25109. Epub 2021 Jun 8.
To develop a semi-automatic framework for quantitative analysis of biochemical properties and thickness of femur cartilage using magnetic resonance (MR) images and evaluate its potential for femur cartilage classification into asymptomatic (AS), early osteoarthritis (OA), and advanced OA groups. In this study, knee joint MRI data (fat suppressed-proton density-weighted and multi-echo T2-weighted images) of eight AS-volunteers (data acquired twice) and 34 OA patients including 20 early OA (16 Grade-I and 4 Grade-II), 14 advanced-OA (Grade-III) were acquired at 3.0T MR scanner. Modified Outerbridge classification criteria was performed for the clinical evaluation of data by an experienced radiologist. Cartilage segmentation, T2-mapping, 2D-WearMap generation, and subregion analysis were performed semi-automatically using in-house developed algorithms. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were computed for testing the reproducibility of T2 values. One-way analysis of variance with Tukey-Kramer post hoc test was performed for evaluating the differences among the groups. The performance of individual T2 and thickness, as well as their combination using logistic regression, were evaluated with receiver operating characteristics (ROC) curve analysis. The interscan agreement based on the ICC index was 0.95 and the CV was 2.45 ± 1.33%. T2 mean of values greater than 75th percentile showed sensitivity and specificity of 94.1% and 81.3% (AUC = 0.93, cut-off value = 47.9 ms) in differentiating AS volunteers versus OA group, while sensitivity and specificity of 90.0% and 81.3% (AUC = 0.90, cut-off value = 47.9 ms) in differentiating AS volunteers versus early OA groups, respectively. In the differentiation of early OA versus advanced-OA group, ROC results of combination (T2 and thickness) showed the highest sensitivity and specificity of 85.7%, and 70.0% (AUC = 0.79, cut-off value = 0.39) compared with individual T2 and thickness features, respectively. A computer-aided quantitative evaluation of femur cartilage degeneration showed promising results and can be used to assist clinicians in diagnosing OA.
开发一种半自动框架，用于使用磁共振（MR）图像对股骨软骨的生化特性和厚度进行定量分析，并评估其将股骨软骨分类为无症状（AS）、早期骨关节炎（OA）和晚期OA组的潜力。在本研究中，在3.0T MR扫描仪上采集了8名AS志愿者（数据采集两次）以及34名OA患者（包括20名早期OA患者（16名I级和4名II级）、14名晚期OA患者（III级））的膝关节MRI数据（脂肪抑制质子密度加权图像和多回波T2加权图像）。由经验丰富的放射科医生根据改良的Outerbridge分类标准对数据进行临床评估。使用内部开发的算法半自动地进行软骨分割、T2映射、二维磨损图生成和子区域分析。计算组内相关系数（ICC）和变异系数（CV）以测试T2值的可重复性。使用Tukey-Kramer事后检验进行单因素方差分析，以评估各组之间的差异。使用受试者操作特征（ROC）曲线分析评估单个T2和厚度的性能，以及使用逻辑回归对它们进行组合的性能。基于ICC指数的扫描间一致性为0.95，CV为2.45±1.33%。T2值大于第75百分位数时，在区分AS志愿者与OA组时，敏感性和特异性分别为94.1%和81.3%（AUC = 0.93，截断值 = 47.9 ms），而在区分AS志愿者与早期OA组时，敏感性和特异性分别为90.0%和81.3%（AUC = 0.90，截断值 = 47.9 ms）。在区分早期OA与晚期OA组时，组合（T2和厚度）的ROC结果显示，与单个T2和厚度特征相比，敏感性和特异性最高，分别为85.7%和70.0%（AUC = 0.79，截断值 = 0.39）。对股骨软骨退变的计算机辅助定量评估显示出有前景的结果，可用于协助临床医生诊断OA。
