Development of Machine Learning Models aiming at Knee Osteoarthritis Diagnosing: an MRI Radiomics Analysis

Abstract

Background To develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis. Methods This retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with available MRI image data, where radiomics features were extracted and then filtered. Intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features, and a threshold of 0.8 was set. The training and validation cohorts consisted of 117 and 31 cases, respectively. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were Logistic Regression (LR), K-Nearest Neighbor (KNN) and support vector machine (SVM). In each algorithm, ten models derived from all available planes of three joint compartments and their various combinations were respectively constructed for comparative analysis. The performance of classifiers was mainly evaluated and compared by receiver operating characteristic (ROC) analysis. Results All models achieved satisfying performances, especially the Final model, where accuracy and area under curve (AUC) of SVM classifier were 0.871, 0.942(0.868-1.000, 95% confidence intervals [CI]) in the validation cohort, and 1.000 and 1.000(1.000–1.000, 95% CI) in the training cohort, respectively. Conclusion The MRI radiomics analysis represented promising performance in non-invasive and preoperative KOA diagnosis, especially when considering all available planes of all three compartments of knee joints.

Radiomics analysis using magnetic resonance imaging of bone marrow edema for diagnosing knee osteoarthritis

“…One study delineated the ROI of the cartilage to construct a model for diagnosing clinical OA. The radiomics feature model performed well in diagnosing clinical OA (AUC, 0.984) (Cui et al, 2023). In addition, a recent study suggested that the texture of the infrapatellar fat pad based on MRI is related to the future development of knee OA and can be used to predict the diagnosis of knee arthritis 1 year later (Ye et al, 2023).…”

Table4.doc

“…One study delineated the ROI of the cartilage to construct a model for diagnosing clinical OA. The radiomics feature model performed well in diagnosing clinical OA (AUC, 0.984) (Cui et al, 2023). In addition, a recent study suggested that the texture of the infrapatellar fat pad based on MRI is related to the future development of knee OA and can be used to predict the diagnosis of knee arthritis 1 year later (Ye et al, 2023).…”

Table1.doc

“…One study delineated the ROI of the cartilage to construct a model for diagnosing clinical OA. The radiomics feature model performed well in diagnosing clinical OA (AUC, 0.984) (Cui et al, 2023). In addition, a recent study suggested that the texture of the infrapatellar fat pad based on MRI is related to the future development of knee OA and can be used to predict the diagnosis of knee arthritis 1 year later (Ye et al, 2023).…”