Associating Knee Osteoarthritis Progression with Temporal‐Regional Graph Convolutional Network Analysis on MR Images

Abstract

BackgroundArtificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability.PurposeTo introduce a temporal‐regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome.Study TypeRetrospective.Population194 OA progressors (mean age, 62 ± 9 years) and 406 controls (mean age, 61 ± 9 years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts.Field Strength/SequenceSagittal 2D IW‐TSE‐FS (IW) and 3D‐DESS‐WE (DESS) at 3T.AssessmentAnatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12‐month, and 24‐month were automatically segmented and served as inputs to form compartment‐based graphs for a TRGCN model, which containing both regional and temporal information. The performance of models based on (i) clinical variables alone, (ii) radiologist score alone, (iii) combined features (containing i and ii), (iv) composite TRGCN (combining TRGCN, i and ii), (v) radiomics features, (vi) convolutional neural network based on Densenet‐169 were compared.Statistical TestsDeLong test was performed to compare the areas under the ROC curve (AUC) of all models. Additionally, interpretability analysis was done to evaluate the contributions of individual regions. A P value <0.05 was considered significant.ResultsThe composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all P < 0.05). Interpretability analysis highlighted cartilage's importance over other structures (42%–45%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%–67% vs. 12%–37%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12‐month: 52% vs. 39%, 24‐month: 31% vs. 48%).Data ConclusionThe composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression.Level of Evidence4.Technical EfficacyStage 2.