{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:39:04Z","timestamp":1787243944767,"version":"build-2736575974"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To introduce a temporal\u2010regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>194 OA progressors (mean age, 62\u2009\u00b1\u20099\u2009years) and 406 controls (mean age, 61\u2009\u00b1\u20099\u2009years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>Sagittal 2D IW\u2010TSE\u2010FS (IW) and 3D\u2010DESS\u2010WE (DESS) at 3T.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Anatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12\u2010month, and 24\u2010month were automatically segmented and served as inputs to form compartment\u2010based 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\u2010169 were compared.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>P<\/jats:italic>\n                      value &lt;0.05 was considered significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05). Interpretability analysis highlighted cartilage's importance over other structures (42%\u201345%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%\u201367% vs. 12%\u201337%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12\u2010month: 52% vs. 39%, 24\u2010month: 31% vs. 48%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>4.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>Stage 2.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.29412","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T06:16:14Z","timestamp":1714457774000},"page":"378-391","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Associating Knee Osteoarthritis Progression with Temporal\u2010Regional Graph Convolutional Network Analysis on\n                    <scp>MR<\/scp>\n                    Images"],"prefix":"10.1002","volume":"61","author":[{"given":"Jiaping","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Medical Imaging The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics\u00b7 Guangdong Province)  Guangzhou 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