Prediction Of Symptoms Progression For The Patients With Knee Osteoarthritis Based On The Quantitative Structural Features: Data From The Fnih Oa Biomarkers Consortium

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.