Machine learning to predict incident radiographic knee osteoarthritis over 8 Years using combined MR imaging features, demographics, and clinical factors: data from the Osteoarthritis Initiative

Abstract

Objective: To develop a machine learning-based prediction model for incident radiographic osteoarthritis (OA) of the knee over 8 years using MRI-based cartilage biochemical composition and knee joint structure, demographics, and clinical predictors including muscle strength and symptoms.

Design: Individuals (n = 1,044) with baseline Kellgren Lawrence (KL) grade 0-1 in the right knee from the Osteoarthritis Initiative database were analyzed. 3T MRI at baseline was used to quantify knee cartilage T₂, and Whole-Organ Magnetic Resonance Imaging Scores (WORMS) were obtained for cartilage, meniscus, and bone marrow. The outcome was set as true if a subject developed KL grade 2-4 OA in the right knee over 8 years (n = 183) and false if the subject remained at KL 0-1 over 8 years (n = 861). We developed and compared three models: Model 1: 112 predictors based on OA risk factors; Model 2: top ten predictors based on feature importance score from Model 1 and clinical relevance; Model 3: Model 2 without the imaging predictors. We compared the models using the area under the ROC curve derived from hold-out data.

Results: The 10-predictor model (Model 2, that includes cartilage and meniscus WORMS scores and cartilage T₂) had a slightly lower AUC (0.772) compared to the model with 112 predictors (Model 1: AUC = 0.792, p = 0.739); and had a significantly higher AUC compared to the model without MR imaging predictors (Model 3, AUC = 0.669, p = 0.011).

Conclusions: A 10-predictor model including MRI parameters coupled with demographics, symptoms, muscle, and physical activity scores provides good prediction of incident radiographic OA over 8 years.

Keywords: Cartilage imaging; MRI; Machine learning; Osteoarthritis; XGboost.