Cartilage repair can greatly alleviate the symptoms of the patients with knee osteoarthritis (KOA). However, some imaging results suggest that the patients with obvious cartilage repair may receive insignificant or even no improvement in their symptoms. This study aims to explore the possible reasons based on the structural feature of the knee joint and construct the models used to predict the progression of knee joint symptoms. 551 subjects from Osteoarthritis Biomarkers Consortium FNIH Project in the Osteoarthritis Initiative (OAI) were included and divided into training and test sets. A total of 153 structural features from five quantitative structural feature sets were included to access the structural characteristics of the knee joints. The Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index was used to evaluate the symptoms of the knee joints. A three-step feature selection method were used to screen the structural features. Finally, Naive Bayes (NB), logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM) and random forest (RF) models were constructed based on the selected features, and then compared using the receiver operating characteristic (ROC) curve. The distribution in the demographics and WOMAC symptoms scores of the participants was consistent in the training and test sets. Two demographic features and several structural features were selected using the three-step feature selection method. Among the constructed models, the models used for the progression prediction of pain, stiffness and total scores were better than that of physical function. The performance of RF model was the best while SVM model was the second best, and the performance of the remaining three models in predicting the progression of knee symptoms is indistinguishable. Structural feature-based models for the prediction of knee joint symptoms’ progression were constructed and compared. The constructed model showed good feasibility and accuracy, and may assist clinicians to predict the occurrence or progression of the knee joints symptoms in the evaluation and prognosis of cartilage repair.

中文翻译：

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基于定量结构特征的膝关节骨性关节炎患者症状进展预测：来自 FNIH OA 生物标志物联盟的数据

软骨修复可以大大缓解膝骨关节炎（KOA）患者的症状。然而，一些影像学结果表明，软骨修复明显的患者症状可能没有明显改善甚至没有改善。本研究旨在根据膝关节结构特征探讨可能的原因，并构建用于预测膝关节症状进展的模型。包括来自骨关节炎倡议 (OAI) 中骨关节炎生物标志物联盟 FNIH 项目的 551 名受试者，并将其分为训练集和测试集。包括来自五个定量结构特征集的总共 153 个结构特征，以获取膝关节的结构特征。西安大略和麦克马斯特大学 (WOMAC) 骨关节炎指数用于评估膝关节的症状。采用三步特征选择方法筛选结构特征。最后，朴素贝叶斯 (NB)、逻辑回归 (LR)、ķ-基于所选特征构建最近邻（KNN）、支持向量机（SVM）和随机森林（RF）模型，然后使用接收器操作特征（ROC）曲线进行比较。参与者的人口统计和 WOMAC 症状评分的分布在训练和测试集中是一致的。使用三步特征选择方法选择了两个人口统计特征和几个结构特征。在构建的模型中，用于疼痛、僵硬和总分的进展预测的模型优于身体功能的模型。RF模型的性能最好，SVM模型次之，其余三个模型在预测膝关节症状进展方面的性能无法区分。构建并比较了预测膝关节症状进展的基于结构特征的模型。所构建的模型具有良好的可行性和准确性，可帮助临床医生预测膝关节症状的发生或进展，对软骨修复的评估和预后进行评估。