ABSTRACT
BACKGROUND: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability. PURPOSE: To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome. STUDY TYPE: Retrospective. POPULATION: 194 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/SEQUENCE: Sagittal 2D IW-TSE-FS (IW) and 3D-DESS-WE (DESS) at 3T. ASSESSMENT: Anatomical 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 TESTS: DeLong 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. RESULTS: The 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 CONCLUSION: The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression. TECHNICAL EFFICACY: Stage 2.
ABSTRACT
BACKGROUND: The long-term outcomes after stereotactic radiosurgery (SRS) for pediatric brain arteriovenous malformations (AVMs) remain poorly understood given the paucity of longitudinal studies. A systematic review was conducted to pool cumulative incidences for all outcomes. METHODS: PubMed, Embase, and Web of Science were queried to systematically extract potential references. The articles relating to AVMs treated via SRS were required to be written in English, involve pediatric patients (<18 years of age), and include a mean follow-up period of >5 years. Individual patient data were obtained to construct a pooled Kaplan-Meier plot on obliteration rates over time. RESULTS: Among the 6 studies involving 1315 pediatric patients averaging a follow-up period of 86.6 months (range, 6-276), AVM obliteration was observed in 66.1% with cumulative probabilities of 48.28% (95% confidence interval [CI], 41.89-54.68), 76.11% (95% CI, 67.50-84.72), 77.48% (95% CI, 66.37-88.59) over 3, 5, and 10 years, respectively. The cumulative incidence of post-SRS hemorrhage, tumors, cysts, and de novo seizures was 7.2%, 0.3%, 1.6%, and 1.5%, respectively. The cumulative incidence of radiation-induced necrosis, edema, radiologic radiation-induced changes (RICs), symptomatic RICs, and permanent RICs were 8.0%, 1.4%, 28.0%, 8.7%, and 4.9%, respectively. CONCLUSIONS: Studies assessing long-term outcomes after SRS are moderate in quality and retrospective. Thus, interpretation with caution is advised given the variable degree of loss to follow-up, which suggests that complication rates may be higher than the values stated in the literature. Future prospective studies are needed to validate these findings.
