Osteoarthritis imaging最新文献

{"title":"DATA-DRIVEN DISCOVERY OF KNEE OSTEOARTHRITIS SUBGROUPS VIA CLUSTER ANALYSIS OF MRI BIOMARKERS","authors":"J.E. Collins , L.A. Deveza , D.J. Hunter , V.B.K. Kraus , A. Guermazi , F.W. Roemer , J.N. Katz , T. Neogi , E. Losina","doi":"10.1016/j.ostima.2025.100351","DOIUrl":"10.1016/j.ostima.2025.100351","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Identifying structural morphotypes in knee OA, subgroups defined by anatomical and morphological attributes, may facilitate personalized treatment by aligning specific patterns of joint damage with treatment mechanism of action. Cluster analysis is a type of unsupervised machine learning used to uncover subgroups and may provide insight into structural morphotypes in knee OA.</div></div><div><h3>OBJECTIVE</h3><div>To use cluster analysis to investigate possible subgroups defined by imaging features in a cohort of persons with knee OA.</div></div><div><h3>METHODS</h3><div>We used data from the PROGRESS OA study, the second phase of the FNIH OA Biomarkers Consortium project, which includes data from the placebo arms of several completed RCTs testing various therapeutic interventions for symptomatic knee OA. MRIs were obtained at baseline and read according to the MRI OA Knee Score (MOAKS) by an experienced radiologist. We included MOAKS assessments of BML size, osteophytes, cartilage, Hoffa-synovitis, effusion-synovitis, and meniscus in the clustering algorithms. Raw ordinal MOAKS scores were used in this analysis. We used Partitioning Around Medoids (PAM) for clustering. PAM is similar to K-means, but instead of defining cluster center as the centroid (mean), the medoid is used, making the method more robust to outliers and appropriate for non-Gaussian data. We undertook several approaches to clustering to perform dimension reduction and incorporate correlations between MOAKS scores A: PAM on Gower’s distance; B: PAM on the dissimilarity matrix from Spearman correlation; C: PAM after non-metric multidimensional scaling (NMDS) using Gower distance for dimension reduction. These approaches aimed to uncover patterns orthogonal to disease severity. The number of clusters was selected based on silhouette width and the gap statistic. Silhouette scores 0.25 to 0.5 indicate weak to reasonable fit.</div></div><div><h3>RESULTS</h3><div>356 participants from four RCTs were included, 138 (39%) with KLG 2 radiographs and 218 (61%) with KLG 3. The cohort was 57% female with average age 62 (SD 8). The number of clusters ranged from 2 to 3 depending on method. There was modest to high overlap between clustering solutions from different methods, suggesting some stability of clustering solutions. Average silhouette scores were 0.19, 0.13, 0.40 for methods A, B, and C, suggesting poor to modest fit. This could suggest weak structure, overlapping clusters, or need for additional dimension reduction. Methods A and C had one cluster dominated (>95% KLG 3) by KLG 3 knees (Figure 1). Investigation of MOAKS assessments by cluster for each of three clustering solutions is shown in Table 1. For example, method C suggested 3 clusters. Clusters 1 and 2 are both approximately 55-60% KLG 2. Cluster 1 has more lateral cartilage damage, and higher BML and osteophyte scores, while cluster 2 has more medial cartilage damage and medial meniscal dama","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100351"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"REPEATABILITY OF CT OSTEOARTHRITIS KNEE SCORE (COAKS) MULTICOMPONENT MEASURES","authors":"T.D. Turmezei , A. Boddu , N.H. Degala , J.A. Lynch , N.A. Segal","doi":"10.1016/j.ostima.2025.100325","DOIUrl":"10.1016/j.ostima.2025.100325","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>The CT Osteoarthritis Knee Score (COAKS) is a semiquantitative system for grading structural disease features in knee OA from weight bearing CT (WBCT). Previous work has demonstrated excellent inter- and intra-observer reliability of COAKS with the aid of a feature scoring atlas, but test-retest repeatability has not yet been evaluated. There is growing interest in multicomponent measures in knee OA imaging research because they may provide granularity in structural feature evaluation, in particular with respect to study baseline stratification and monitoring progression. The multi-feature and multi-compartment nature of COAKS means that it could provide novel insights into OA morphotypes and structural disease progression if found to be robust.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate test-retest agreement of COAKS multicomponent scores based on WBCT imaging.</div></div><div><h3>METHODS</h3><div>14 individuals recruited and consented at the University of Kansas Medical Center had baseline and follow-up WBCT imaging suitable for analysis. Participants were (mean ± SD) 61.3 ± 8.4 years old, with BMI 30.7 ± 4.3 kg/m<sup>2</sup> and had a male:female ratio of 8:6. All scanning was performed on a single XFI WBCT scanner (Planmed Oy, Helsinki, Finland) with the mean ± SD interval between baseline and follow-up attendances 14.9 ± 8.1 days. A Synaflexer<sup>TM</sup> device was used to standardize knee positioning during scanning. Imaging acquisition parameters were 96 kV tube voltage, 51.4 mA tube current, 3.5 s exposure time. A standard bone algorithm was applied for reconstruction with 0.3 mm isotropic voxels and a 21 cm vertical scan range. All scans were anonymised prior to analysis both according to the individual and imaging attendance. All knees were reviewed for COAKS by an experienced musculoskeletal radiologist (T.D.T.). Scores were recorded in a cloud-based file on Google Sheets (alongside the feature atlas in Google Docs) and read by custom MATLAB scripts to generate baseline versus follow-up difference plots and intraclass correlation coefficients for absolute agreement from a single observer, Shrout-Fleiss ICC(3,1). Scores for individual COAKS features (JSW, osteophytes, subchondral cysts, subchondral sclerosis) were combined across compartments. Compartment scores (medial tibiofemoral, lateral tibiofemoral, patellofemoral, proximal tibiofibular) were combined across features. Multicomponent scores were also summated for the whole tibiofemoral compartment (medial-lateral combined) and from across the whole knee joint.</div></div><div><h3>RESULTS</h3><div>ICC values were excellent (>0.81) for all multicomponent scores apart from subchondral sclerosis combined across all compartments (0.69, 0.43-0.84) and all features combined at the proximal tibiofibular joint (0.65, 0.38-0.82). Best agreement was seen for osteophytes combined across all compartments (0.93, 0.85-0.96) (Figure 1), all features comb","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100325"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PREDICTING KNEE OSTEOARTHRITIS PROGRESSION USING EXPLAINABLE MACHINE LEARNING AND CLINICAL IMAGING DATA","authors":"R.E. Harari , J. Collins , S.E. Smith , S. Wells , J. Duryea","doi":"10.1016/j.ostima.2025.100348","DOIUrl":"10.1016/j.ostima.2025.100348","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Accurate prediction of knee osteoarthritis (KOA) progression remains a clinical challenge due to its heterogeneous nature and discordance between structural and symptomatic outcomes. Integrated imaging and machine learning (ML) approaches may enhance prognostic modeling but often suffer from limited interpretability or reliance on static features.</div></div><div><h3>OBJECTIVE</h3><div>We aim to develop explainable ML models for predicting KOA progression using baseline and longitudinal imaging and clinical features. This study also aims to identify key imaging biomarkers associated with structural and symptomatic progression.</div></div><div><h3>METHODS</h3><div>Data and 3T MRI measurements from 600 participants in the FNIH OA Biomarkers Consortium were analyzed. Participants were grouped into four progression categories based on 48-month joint space narrowing and WOMAC pain: (1) radiographic + pain progressors, (2) radiographic-only, (3) pain-only, and (4) non-progressors. Two binary classification frameworks were defined: (1) radiographic + pain vs. all others (primary), and (2) all radiographic progressors vs. pain-only + non-progressors (secondary). ML models included Random Forest, XGBoost, logistic regression, decision tree, and multilayer perceptron (MLP). The model used demographic information and imaging features from semi-automated segmentation software. We measured the volume of medial compartment femur cartilage (Cart), bone marrow lesion (BML) in the MF, LF, MT, LT, patella, and trochlea, osteophytes (Ost) in the MF, LF, MT, and LT, Hoffa’s synovitis (HS), and effusion/synovitis (ES). Longitudinal delta values were computed over 24 months. Performance was assessed via 10-fold stratified cross-validation (AUC, F1-score). Explainability tools included SHAP, Gini importance, coefficients, and permutation importance.</div></div><div><h3>RESULTS</h3><div>In the cross-sectional setting, the Random Forest classifier achieved the highest discrimination performance, with AUC values of 0.672 for the primary task (radiographic + pain progressors vs. others) and 0.791 for the secondary task (all radiographic progressors vs. others). The MLP model showed similar results in the secondary task (AUC = 0.743). AUC performance metrics for all models are shown in Table 1. Model performance improved notably when incorporating 24-month changes in imaging features. In the longitudinal analysis, Random Forest again performed best in the secondary task (AUC = 0.873), followed by XGBoost and MLP. The strongest predictors in these models were changes in medial femoral cartilage thickness, medial tibial bone marrow lesions, and osteophyte scores. To better understand the basis of model predictions, we applied four feature ranking methods. Among them, the SHAP method produced the most consistent and clinically interpretable results. As an example, shown in Figure 1 which show top 15 important features, SHAP highlighted 24-month r","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100348"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SIMULTANEOUS 3D CARTILAGE T2 MAPPING AND MORPHOLOGICAL IMAGING WITH RAFO-4 MRI, A MACHINE LEARNING ALGORITHM","authors":"K. Balaji , M. Mendoza , P.M. Vicente , C. Galazis , S. Kukran , A.A. Bharath , P.J. Lally , N.K. Bangerter","doi":"10.1016/j.ostima.2025.100277","DOIUrl":"10.1016/j.ostima.2025.100277","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Cartilage T<sub>2</sub> is a non-invasive, microstructural MRI biomarker for KOA, with elevated T<sub>2</sub> indicating early KOA onset. Cartilage T<sub>2</sub> maps could be used in clinical trials to test a drug candidate’s effect on microstructure. Quantitative DESS (qDESS) is widely used for cartilage imaging as it simultaneously acquires 3D, morphological whole knee images and quantitative T<sub>2</sub> maps in ∼5 minutes. Researchers are also developing T<sub>2</sub> mapping techniques using phase-cycled balanced Steady State Free Precession (pc-bSSFP). It is rapid and has higher SNR efficiency than qDESS, which could lead to better 3D morphological image quality and more reliable T<sub>2</sub> maps. PLANET is a technique that uses a minimum of six different pc-bSSFP acquisitions to analytically calculate T<sub>2</sub>. This is too time-consuming to be clinically feasible. In this study, we trained Random Forest (RaFo) machine learning models to estimate T<sub>2</sub> from fewer pc-bSSFP acquisitions to reduce scan time while still estimating reliable voxel-level T<sub>2</sub> values.</div></div><div><h3>OBJECTIVE</h3><div>1) Train and test RaFo models on simulated 4 and 6 pc-bSSFP data and benchmark performance with PLANET. 2) Test RaFo models on in vivo knee data and benchmark performance with the reference T<sub>2</sub> mapping technique (spin echo), PLANET, and qDESS.</div></div><div><h3>METHODS</h3><div>70,000-sample training and 30,000-sample testing datasets were simulated. Each sample corresponded to 12 different pc-bSSFP measurements of the same voxel location in the tissue. The physics-informed simulated datasets were pre-processed, which included sub-sampling from 12 pc-bSSFP measurements to 4 or 6. RaFo models were then trained to estimate T<sub>2</sub> and tested on these pre-processed datasets. Finally, to evaluate performance on noisier <em>in vivo</em> data, fully sampled knee images of two healthy volunteers (HVs, 2F:24-25) were acquired on a 3T Siemens Verio (Erlangen, Germany) with an 8-channel knee coil using 12 measurements of bSSFP (water excitation, 8.6/4.3 ms TR/TE; 22° flip angle; 1 × 1 × 5 mm<sup>3</sup> voxel volume; 128 × 128 × 130 mm<sup>3</sup>), qDESS (water excitation; 20° flip angle; 21.77 ms TR; 6 ms TE; 364 Hz/Px receiver bandwidth; 0 dummy scans per volume), and a gold-standard spin-echo T<sub>2</sub> mapping approach (2500 ms TR; 15, 45, 75 ms TE, 90° and 180° flip angle) with appropriate ethics approval. All images had 1 × 1 × 5 mm<sup>3</sup> voxel volume and 128 × 128 mm<sup>2</sup> field of view. PLANET was tested on 6 pc-bSSFP measurements (labelled PLANET-6). RaFo models were tested on 4 and 6 bSSFP measurements (labelled RaFo-4 and RaFo-6, respectively).</div></div><div><h3>RESULTS</h3><div>Fig1 shows results from simulated data tests, with similar performance across the RaFo models and PLANET. Fig2 shows the in vivo T<sub>2</sub> maps, with the RaFo models visually","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100277"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE EFFECT OF WEIGHT LOSS AND GLUCAGON-LIKE PEPTIDE-1 RECEPTOR AGONIST ON STRUCTURAL CHANGES IN KNEE OSTEOARTHRITIS: SECONDARY ANALYSIS OF THE RANDOMISED, PLACEBO-CONTROLLED LOSEIT TRIAL","authors":"M.W. Brejnebøl ,&nbsp;T. Haugegaard ,&nbsp;R. Christensen ,&nbsp;H. Gudbergsen ,&nbsp;H. Bliddal ,&nbsp;P. Hansen ,&nbsp;L.E. Kristensen ,&nbsp;C.T. Nielsen ,&nbsp;C.L. Daugaard ,&nbsp;J.U. Nybing ,&nbsp;M. Henriksen ,&nbsp;M. Boesen","doi":"10.1016/j.ostima.2025.100280","DOIUrl":"10.1016/j.ostima.2025.100280","url":null,"abstract":"<div><h3>OBJECTIVE</h3><div>To compare the effect of weight loss and glucagon-like peptide-1 receptor agonist (GLP-1RA) (liraglutide), relative to weight loss and placebo, on structural knee osteoarthritis.</div></div><div><h3>METHODS</h3><div>This secondary analysis followed a superiority framework of data from the LOSEIT trial, a randomised, parallel-group, placebo-controlled trial. Participants aged 18 to 74 years with overweight (BMI ≥27 kg/m²), symptomatic and early-to-moderate radiographic knee OA were recruited. They underwent 8-week intensive diet intervention followed by randomisation to receive a GLP-1RA (liraglutide 3 mg/d) or placebo for 52 weeks. The primary outcome was the change in radiographic medial minimal joint space width (mmJSW). Analyses were conducted on the intention-to-treat population.</div></div><div><h3>RESULTS</h3><div>From November 14, 2016, through September 12, 2017, 156 participants were randomly assigned to GLP-1RA (n = 80) or to placebo (n = 76). As reported in the primary analysis of the data, the GLP-1RA group lost more weight than the placebo group (mean difference, - 3.21 kg, 95%CI: - 6.39 to - 0.03; P=0.050). The GLP-1RA group demonstrated an increase in mean mmJSW of 0.22 mm (95%CI: 0.06 to 0.38) while the placebo group did not change (0.07 mm, 95%CI: - 0.11 to 0.25). No evidence of a difference in mean mmJSW was observed between groups (0.15 mm, 95%CI: -0.06 to 0.36; P=0.17).</div></div><div><h3>CONCLUSION</h3><div>While the results indicate a potentially favourable effect on mmJSW within the GLP-1RA group, the observed difference in structural knee OA changes on radiographs compared to placebo did not reach statistical significance.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100280"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Posters","authors":"","doi":"10.1016/S2772-6541(25)00105-9","DOIUrl":"10.1016/S2772-6541(25)00105-9","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100365"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"POTENTIAL IMPACT OF DIABETES MELLITUS ON CARTILAGE THICKNESS AND COMPOSITION IN SUBJECTS WITH AND WITHOUT OSTEOARTHRITIS – A MATCHED CASE-CONTROL STUDY","authors":"F. Eckstein ,&nbsp;W. Wirth ,&nbsp;A. Eitner","doi":"10.1016/j.ostima.2025.100286","DOIUrl":"10.1016/j.ostima.2025.100286","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Diabetes mellitus (DM) and osteoarthritis (OA) are interconnected through metabolic and inflammatory pathways that independently contribute to joint pain and structural degeneration [1]. Elevated blood glucose can induce systemic inflammation and oxidative stress, promoting joint symptoms and cartilage damage. Also, DM is frequently associated with obesity, potentially increasing mechanical loading and cartilage wear, particularly in weight-bearing joints.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To assess the association of DM with femorotibial cartilage morphology and composition (T2 relaxation time), compared with matched controls without DM. Matching included age, sex, obesity status, knee pain, and radiographic OA (ROA) status. Analyses were stratified by the presence or absence of ROA.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Participants were selected from the Osteoarthritis Initiative (OAI) [2]. A total of 362 individuals with DM were identified based on the Charlson Comorbidity Index. Of those, 260 were successfully matched to DM-negative controls based on the same/similar sex, age (±5 years), BMI (±5 kg/m²), WOMAC pain score (±5 on a 0–100 scale), pain frequency (±1 on a 0–2 scale), body height (±10 cm), and Kellgren-Lawrence (KL) grade [2]. Femorotibial cartilage thickness was derived from sagittal DESSwe MRIs at 3T using fully automated segmentation methodology. This involved a deep-learning-based pipeline combining 2D U-Net segmentation of subchondral bone and cartilage with atlas-based post-processing for subchondral bone area reconstruction [3]. Laminar cartilage T2 (deep 50%, superficial 50%) were calculated from MESE MRI (7 echoes), also using automated segmentation [3]. Statistical comparisons between DM and non-DM subjects were performed using paired t-tests, without correction for multiple comparisons across joint regions. For cartilage thickness, analyses were stratified by ROA status (KLG 2–4 vs. KLG 0–1). T2 analysis was restricted to KLG 0–2, as laminar T2 becomes less interpretable once cartilage loss is present.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;DM participants were 63.4 ± 8.9y old, 53% female, BMI 31.5±4.5 kg/m². A total of 244 matched pairs were available with cartilage data at baseline (234 with thickness, 222 with T2; 78x KLG0, 46 × 1, 62 × 2, 52 × 3, 6x KLG4). In non-arthritic participants, the medial cartilage thickness was 3.45 mm (95% CI: 3.35–3.55) in DM subjects and 3.43 mm (3.33–3.54) in controls. Lateral thickness was 3.90 mm (3.80–4.00) in DM vs. 3.87 mm (3.76–3.97) in controls. Among ROA cases, medial thickness was 3.16 mm (3.03–3.29) in DM vs. 3.30 mm (3.17–3.42) in controls; lateral thickness was 3.68 mm (3.53–3.83) vs. 3.76 mm (3.64–3.88), respectively. None of the DM vs. non-DM differences reached statistical significance. In the 170 matched pairs that were KLG 0–2, no significant differences in cartilage T2 were identified: In the medial superficial layer, T2 was 48.2 ms (47","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100286"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TOWARD OPENLY AVAILABLE KNEE MRI SEGMENTATIONS FOR THE OAI: MULTI-MODEL EVALUATION AND CONSENSUS GENERATION ON 9,360 SCANS","authors":"M.S. White ,&nbsp;K.T. Gao ,&nbsp;V. Pedoia ,&nbsp;S. Majumdar ,&nbsp;G.E. Gold ,&nbsp;A.S. Chaudhari ,&nbsp;A.A. Gatti","doi":"10.1016/j.ostima.2025.100330","DOIUrl":"10.1016/j.ostima.2025.100330","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Many deep learning methods exist for segmentation of bone and cartilage in knee MRI, but their agreement and impact on quantitative metrics (e.g., cartilage thickness) remain unclear. Prior studies have not investigated whether combining segmentations from independent deep learning models can improve sensitivity to detect clinically relevant differences. Understanding these effects in large cohorts is essential to guide deep learning in OA research and clinical trials.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To generate consensus segmentations from independent deep learning models developed at Stanford and UCSF, evaluate agreement between bone and cartilage segmentations across all models, and assess each method’s sensitivity to detect cartilage thickness differences between KL2 and KL3 knees.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Bone and cartilage segmentations of 9360 knees from the OAI baseline dataset were independently generated in prior work by Stanford and UCSF using separately validated deep learning models. A consensus segmentation was generated using the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm, with the threshold tuned to minimize cartilage volume differences between the two models. Segmentations were compared using volume differences (%), Dice Similarity Coefficient (DSC), and average symmetric surface distance (ASSD). Mean cartilage thickness was computed in sub-regions (femur: anterior, medial/lateral weight-bearing, posterior; tibia: medial and lateral, and patella) and compared using Pearson correlations and intraclass correlation coefficients (ICC). Each method’s (UCSF, Stanford, and STAPLE’s) sensitivity to detect between group (KL2 and KL3) differences in cartilage thickness was assed using effect sizes (Cohen’s d).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Comparing Stanford and UCSF models, bone demonstrated better overlap (DSC = 0.95-0.97) compared to cartilage (DSC = 0.79-0.82). However, cartilage had smaller volume differences (-0.2-1.9% vs. 2.5-6.2%) and lower ASSD (0.24-0.33 mm vs. 0.33-0.47 mm) relative to bone. Both Stanford vs. STAPLE and UCSF vs. STAPLE yielded better segmentation agreement (higher DSC, lower ASSD) compared to Stanford vs. UCSF, despite larger volume differences (Table 1A). Compared to one another, Stanford and UCSF cartilage thickness measurements had high correlation (r = 0.96-0.99) and agreement (ICC = 0.96-0.99, mean differences &lt; 0.04 mm). STAPLE produced systematically greater thickness values (mean difference = 0.16 ± 0.08 mm), and slightly lower ICCs (ICC = 0.88-0.96), and correlations (r = 0.92-.97) when compared with Stanford or UCSF. Effect sizes for mean cartilage thickness between KL2 and KL3 knees were small (Cohen’s d &lt; 0.5), except for the medial weight-bearing femur, which had moderate effects for Stanford (-0.60) and UCSF (-0.58), and small-to-moderate for STAPLE (-0.48; Table 1B).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;C","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100330"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SEX-SPECIFIC CONTINUOUS JOINT SPACE WIDTH: AN ALTERNATIVE TO RHOA GRADING","authors":"F. Boel ,&nbsp;M.A. van den Berg ,&nbsp;N.S. Riedstra ,&nbsp;M.M.A. van Buuren ,&nbsp;J. Tang ,&nbsp;H. Ahedi ,&nbsp;N. Arden ,&nbsp;S.M.A. Bierma-Zeinstra ,&nbsp;C.G. Boer ,&nbsp;F.M. Cicuttini ,&nbsp;T.F. Cootes ,&nbsp;K.M. Crossley ,&nbsp;D.T. Felson ,&nbsp;W.P. Gielis ,&nbsp;J.J. Heerey ,&nbsp;G. Jones ,&nbsp;S. Kluzek ,&nbsp;N.E. Lane ,&nbsp;C. Lindner ,&nbsp;J.A. Lynch ,&nbsp;R. Agricola","doi":"10.1016/j.ostima.2025.100279","DOIUrl":"10.1016/j.ostima.2025.100279","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;The reported prevalence of radiographic hip OA (RHOA) varies widely in literature and depends on the specific study population. The KLG and (modified) Croft grade are commonly used to quantify RHOA. Both these scoring systems are inherently subjective, and the reproducibility is largely dependent on the expertise of the reader. Furthermore, both of these RHOA grading system emphasize different features of RHOA, making them difficult to compare. Using automated RHOA grade would reduce subjectivity and allow for fast, reproducible, and reliable assessment of radiographs. Since JSW currently demonstrates the highest reliability as a ROA describing feature, utilizing continuous JSW measurements could be a promising step towards achieving an automated RHOA grade.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To investigate the association between baseline demographics, RHOA, and automated, continuous JSW.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;We pooled individual participant data from two prospective cohort studies within the Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH consortium). Both cohorts have standardized weight-bearing anteroposterior (AP) pelvic radiographs available at baseline, 4-5 years, and 8 years follow-up. JSW measurements were automatically determined on the AP radiographs based on landmarks on the acetabular sourcil and the femoral head contour. Four different JSW measurements were determined for each hip, namely at the most medial point, in the center and at the most lateral point of the sourcil, and the minimal JSW (Fig 1). RHOA was scored by KLG or modified Croft grade. Based on the baseline and follow-up RHOA grades, the RHOA pattern of the hip was defined as “no definite RHOA” (KLG/Croft &lt; 2 at all timepoints), “baseline RHOA” (KLG/Croft ≥ 2 at baseline), or “incident RHOA” (KLG/Croft ≥ 2 at follow-up). Hips were included for analysis if they had JSW measurements available at all three time points, and RHOA grades available at baseline and follow-up. The association between baseline age, body mass index (BMI), and the RHOA pattern, and each definition of JSW over time was estimated using linear mixed-effects models (LMMs). The analyses were stratified by sex due to known differences in JSW and OA risk in males and females. The random effects included follow-up time, cohort, and participant, accounting for the repeated measurements and cohort clustering. No RHOA was defined as the reference category for RHOA pattern. The resulting model coefficients with 95% confidence intervals (CI) were presented.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;A total of 2,895 participants were included in the current study. 3,368 hips of 1,698 females were included, with a mean baseline age of 60 ± 8 years, a mean baseline BMI of 27.8 ± 5.0 kg/m&lt;sup&gt;2&lt;/sup&gt;, 4.3% had baseline RHOA, and 3.9% had incident RHOA at follow-up. The JSW narrowed on average in all four locations, and the highest preval","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100279"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LEVI-04 REDUCES BONE MARROW LESION AREA AND PRESENCE IN KNEE OSTEOARTHRITIS: RESULTS FROM A PHASE II RCT","authors":"S.L. Westbrook ,&nbsp;A. Guermazi ,&nbsp;P.G. Conaghan","doi":"10.1016/j.ostima.2025.100340","DOIUrl":"10.1016/j.ostima.2025.100340","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Bone marrow lesions (BMLs), detectable on MRI as areas of ill-defined high signal intensity on fluid-sensitive sequences, are a common feature of osteoarthritis (OA), representing areas of increased bone turnover, oedema, and fibrosis. BMLs are prevalent in ∼80% of symptomatic knee OA patients, correlate with radiographic severity (Kellgren-Lawrence [KL] grade) and knee pain. Changes in BMLs are associated with fluctuations in knee pain. Excess neurotrophins (NTs) are implicated in OA pain. LEVI-04, a first-in-class p75NTR-Fc fusion protein that supplements endogenous p75NTR, provides analgesia primarily via inhibition of neurotrophin-3 (NT-3) activity. In this Phase II RCT, LEVI-04 demonstrated statistically significant and clinically meaningful improvements versus placebo for the primary endpoint (WOMAC pain) and secondary endpoints including WOMAC physical function and stiffness, patient global assessment (PGA) and pain on movement (StEPP) across all doses. LEVI-04 was generally well tolerated, with no increased incidence of SAEs, TEAEs, or AESIs concerning joint pathologies compared to placebo.&lt;sup&gt;1&lt;/sup&gt;&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This analysis investigated LEVI-04′s effects on BMLs in people with painful knee OA.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;518 participants with symptomatic knee OA (WOMAC pain ≥ 4/10, KL grade ≥ 2) were enrolled in a Phase II multicentre randomized double-blinded placebo-controlled trial. Participants received placebo or LEVI-04 (0.3, 1, or 2 mg/kg) every 4 weeks through week 16. BML area (mm²) was measured in a blinded fashion from coronal proton density-weighted fat-suppressed (PD-FS) sequences (slice thickness 3 mm, TE/TR 35/3000 ms) of the target knee at baseline and week 20. For each participant, the BML area was determined as the largest area within the MRI sequence of ill-defined high signal intensity of the subchondral bone marrow, and without presence of a fracture line. The perimeter of each BML was highlighted and the area measured electronically using IAG Dynamika Software™. For BML presence, participants were categorized as BML positive if one or more lesions were identified in the target knee. The presence of BML and change in BML area were assessed in response to LEVI-04.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;BML area was greater in knees with higher KL grade (figure 1). The presence of BMLs at baseline was similar across treatment and placebo groups (74-79%). At week 20, there was a significant and dose-dependent reduction in the proportion of patients with BMLs in the LEVI-04 groups (figure 2). Furthermore, a statistically-significant, dose-dependent reduction in mean BML area from baseline to week 20 was observed in LEVI-04 groups compared to placebo (figure 3).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;In this Phase II trial, a statistically significant and dose-dependent reduction in both the presence of BMLs and BML area was seen for all LEV-04 treatment","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100340"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}