Osteoarthritis imaging最新文献

{"title":"TRANSLATION OF X-RAY TO MRI: DIAGNOSTIC PERFORMANCE OF MRI-DEFINED SIMULATED KELLGREN-LAWRENCE GRADING","authors":"F.W. Roemer , A. Guermazi , C.K. Kwoh , S. Demehri , D.J. Hunter , J.E. Collins","doi":"10.1016/j.ostima.2025.100312","DOIUrl":"10.1016/j.ostima.2025.100312","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>While it has been acknowledged that mild-to-moderate radiographic disease severity of knee osteoarthritis (OA), i.e. knees with grades 2 and 3 on the Kellgren-Lawrence (KL) scale – reflect a wide spectrum of tissue damage, it is unknown whether a knee MRI can easily be translated into a specific radiographic (r) KL grade (KLG). In order to potentially use MRI as a single screening tool for eligibility in clinical trials, it is necessary to define which knees correspond to the current inclusion criteria of rKLG 2 and 3.</div></div><div><h3>OBJECTIVE</h3><div>The aim of this study was to assess the diagnostic performance of a priori-determined definitions of MRI-assessed KLG based on osteophytes (OPs) and cartilage damage in the tibiofemoral joint (TFJ).</div></div><div><h3>METHODS</h3><div>We included MRI readings from the following Osteoarthritis Initiative substudies: FNIH Biomarker consortium, POMA and BEAK. Included are visits with centrally read rKLG and available MOAKS readings. In order to match the anteroposterior (a.p.) radiograph, four locations for OPs assessed in the coronal plane (central medial femur, central medial tibia, central lateral femur, central lateral tibia) were considered. Eight subregions were considered for cartilage damage to mirror the weight bearing tibiofemoral joints on X-ray: anterior medial tibia, central medial tibia, posterior medial tibia, central medial femur, anterior lateral tibia, central lateral tibia, posterior lateral tibia and central lateral femur. Cartilage damage was classified as minor: focal damage only (MOAKS 0, 1.0, 1.1); moderate: damage with no advanced full thickness wide-spread damage (MOAKS 2.0, 2.1, 3.0, 3.1); and severe: full thickness wide-spread damage (MOAKS 2.2, 3.2, 3.3).</div><div>The definitions were derived based on expert consensus opinion as follows:</div><div>MRI KL0: no OP (=grade 0 in all 4 locations), minor cartilage damage only</div><div>MRI KL1: grade 1 OP in at least 1 of 4 TFJ locations, maximum OP grade 1, minor cartilage damage only</div><div>MRI KL2: grade 1, 2 or 3 OP in at least 1 of 4 TFJ locations, moderate cartilage damage</div><div>MRI KL2a (“atrophic”): no OP (=grades 0 in all 4 TFJ locations), moderate cartilage damage</div><div>MRI KL 3: grade 1, 2 or 3 OP in at least 1 of 4 TFJ locations, severe cartilage damage in at least 1 of 8 subregions.</div><div>MRI KL3a (“atrophic”): no OP (=grades 0 in all 4 TFJ locations), severe cartilage damage in at least 1 of 8 subregions</div><div>MRI KL 4: grade 1, 2 or 3 OP in at least 1 of 4 TFJ locations, severe cartilage damage in at least 2 of 4 corresponding subregions medially or laterally or both.</div><div>Sensitivity, specificity, negative and positive predictive values were determined using radiographic KLG as the reference.</div></div><div><h3>RESULTS</h3><div>In total, the dataset includes 4924 visits from 1981 participants contributing 2276 knees for up to 4 timepoints. The rKL dis","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100312"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523432","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":"PHOTON-COUNTING CT-BASED TRABECULAR BONE ANALYSIS IN THE KNEE: A COMPARATIVE STUDY OF ADVANCED OSTEOARTHRITIS AND HEALTHY CONTROLS","authors":"M. Jarraya , W. Issa , C. Chane , A. Zheng , D. Guermazi , K. Sariahmed , M. Mohammadian , M. Kim , K.A. Flynn , T.L. Redel , F. Liu , M. Loggia","doi":"10.1016/j.ostima.2025.100344","DOIUrl":"10.1016/j.ostima.2025.100344","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>The advent of photon counting CT is a major advance in the development of CT technology. Its enhanced spatial resolution, compared to conventional CT, and its much-reduced radiation dose make it a promising tool for in vivo assessment of bone microarchitecture in clinical settings. For example, prior studies relying on HR-pQCT and Micro CT have shown greater volumetric bone mineral density (vBMD) and trabecular (Tb) thickness (Th) were significantly higher in the medial compartment and associated with increased disease severity. There is no data on trabecular bone structure using photon counting CT in patients with osteoarthritis (OA).</div></div><div><h3>OBJECTIVE</h3><div>To compare High-Resolution PCCT-defined trabecular bone microstructure between patients with advanced OA versus healthy controls.</div></div><div><h3>METHODS</h3><div>We used data from the ongoing DIAMOND knee study which investigates the role of neuroinflammation in chronic postoperative pain after TKR. To date, 9 healthy controls and 36 patients with advanced knee OA scheduled for total knee replacements have been recruited, including 7 patients who underwent unilateral PCCT. All other patients and healthy controls had bilateral knee scans. We used a Naeotom 144 Alpha PCCT scanner manufactured by Siemens Healthineers (Erlangen, Germany). Scans were performed with a tube voltage of (120 keV) and, to provide maximum scan performance and minimum noise deterioration, slice increments of 0.2 were used. We also utilized a slice thickness of 0.2 mm, rotation time 0.5 seconds, and pitch 0.85 Images were reconstructed with sharp bone kernel Br89 and matrix 1024 × 1024.. The field of view varied depending on the patient’s size, thus resulting in a variable voxel in plane dimension (0.2-0.4 mm). Regions of interests were defined for the proximal tibia and distal femur in a stack height defined by slices equivalent to 1/6<sup>th</sup> to 1/4<sup>th</sup> of the measured joint width, prescribed distally or proximally from the joint line, respectively. Images were analyzed using a previously reported iterative threshold-seeking algorithm with 3D connectivity check to separate trabecular bone from marrow. Apparent structural parameters were derived from bone volume (BV), bone surface (BS), and total volume (TV) according to equations by Parfitt’s model of parallel plates (Tb.Th, Tb.Separation, BV/TV). These trabecular bone measures were compared between OA and healthy knees using independent sample t-test or non-parametric Wilcoxon tests, depending on normality assumptions. All of the analyses were performed compartment-wise in all four ROIs. These images analyses steps were derived from methods previously published by Wong et al. (DOI: <span><span>https://doi.org/10.1016/j.jocd.2018.04.001</span><svg><path></path></svg></span>).</div></div><div><h3>RESULTS</h3><div>We analyzed data from 12 knees of 12 patients with advanced knee OA (mean age 66.0 ± 9.4 years","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100344"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523924","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":"UNCOVERING STRUCTURAL DISEASE PATTERNS OF EARLY POST-TRAUMATIC OSTEOARTHRITIS IN A DMM MOUSE MODEL USING CONTRAST-ENHANCED MICRO-COMPUTED TOMOGRAPHY","authors":"J.E. Schadow , E.C. Boersma , A.M. Cagnoni , H. Liu , R.A. Davey , K.S. Stok","doi":"10.1016/j.ostima.2025.100316","DOIUrl":"10.1016/j.ostima.2025.100316","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Contrast-enhanced micro-computed tomography (CECT) is a non-destructive method to assess cartilage degeneration seen in diseases such as OA whilst also allowing for analysis of bone changes [1, 2]. Application has been limited to <em>ex vivo</em> and <em>in situ</em> studies but using CECT <em>in vivo</em> holds the potential to quantify and track structural cartilage and bone changes and illuminate new understanding of disease onset and progression.</div></div><div><h3>OBJECTIVE</h3><div>The aim of this study was to uncover structural disease patterns of early post-traumatic osteoarthritis in a destabilized medial meniscus (DMM) mouse model using time-lapse CECT.</div></div><div><h3>METHODS</h3><div>DMM (n=22) or sham surgery (n=22) was performed on ten-week-old C57Bl/6 mice. A further three mice did not undergo surgery but were euthanized at 10 weeks of age and processed for histology. Of the mice that had surgery, three mice per group were euthanised and processed for histology at seven-, 14-, 21- and 28-days post-surgery. The remaining ten mice per group received an intra-articular injection of Dotarem (Guerbet) and were scanned at 10.4 μm, 70 kVp, 114 μA using microCT (vivaCT80, Scanco Medical AG) at one-day pre-surgery and seven-, 14-, 21-, 28-, and 56-days post-surgery. After scanning at the final timepoint, three mice per group were euthanised after scanning at 56-days post-surgery and processed for histology. Safranin-O histology was used to score joints following the OARSI guidelines [3]. Mean attenuation of cartilage, joint alignment, joint space morphometry, subchondral bone morphometry, and osteophyte presence were analysed from microCT images. Mixed-effects analysis was used to investigate effects of osteoarthritis, time, and joint side (medial/lateral) on mean attenuation, joint space, subchondral bone, and osteophytes as well as the effects of osteoarthritis and time on joint alignment.</div></div><div><h3>RESULTS</h3><div>OARSI score of medial tibia in DMM OA group increased compared to the lateral side in DMM OA group and medial side of sham controls (Figure 1A). Mean attenuation of medial tibial cartilage in DMM OA mice did not change over time whereas that of sham controls increased over time. The number of voxels in the thinnest joint space layer increased on the medial side of DMM OA group post-surgery but did not change on medial side of sham controls or lateral side of either group (Figure 1B). There was increased variability in dorsal axis and midsagittal axis angles α and γ of DMM OA mice at 14-, 21-, and 28-days post-surgery. There was no difference in shape κ and scale θ of osteophyte thickness distribution of DMM OA tibia compared to sham control, despite osteophyte development on the lateral and medial side of DMM OA tibiae and frontal side of both groups. Cortical porosity and trabecular thickness of medial tibia in DMM OA mice increased over time before decreasing at 56-days post-surg","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100316"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524027","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":"EARLY DETECTION OF KNEE OA – THE ROLE OF A COMPOSITE DISEASE ACTIVITY SCORE: DATA FROM THE OSTEOARTHRITIS INITIATIVE","authors":"J.C. Patarini , T.E. McAlindon , J. Baek , E. Kirillov , N. Vo , M.J. Richard , M. Zhang , M.S. Harkey , G.H. Lo , S.-H. Liu , K. Lapane , C.B. Eaton , J. MacKay , J.B. Driban","doi":"10.1016/j.ostima.2025.100306","DOIUrl":"10.1016/j.ostima.2025.100306","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>BM lesions and effusion-synovitis are frequent and dynamic disease processes detected from early- to late-stage knee OA. These processes are associated with knee symptoms, representing the primary clinical manifestations of OA. Through a systematic and iterative process, we previously developed and validated a composite biomarker – the disease activity score – that combines BM lesions and effusion-synovitis volumes throughout a knee into an efficient continuous single score.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate whether dynamic disease processes (effusion-synovitis volume and BM lesions), summarized by a validated efficient continuous composite score, are present in early OA and prognostic of incident symptomatic knee OA over the subsequent three years.</div></div><div><h3>METHODS</h3><div>We analyzed a convenience sample within the OAI of participants without symptomatic knee OA. Pain assessments and radiographs were collected annually. Among 913 knees (n=572 participants), most were female, white, and had a mean age of 61 (SD=9) and body mass index of 29.4 (SD=4.5) kg/m<sup>2</sup>. MR images were collected at each OAI site using Siemens 3.0 Tesla Trio MR systems. We measured BM lesion and effusion-synovitis volumes on a sagittal IM fat-suppressed sequence (field of view=160mm, slice thickness=3mm, skip=0mm, flip angle=180 degrees, echo time=30ms, recovery time=3200ms, 313 × 448 matrix, x-resolution=0.357mm, y-resolution=0.357mm). Using MR images from the initial visit, we combined effusion-synovitis and BM lesion volumes to calculate a composite score, referred to as the disease activity score. A disease activity score of 0 approximated the average score for a reference sample (n=2,787, 50% had radiographic knee OA, average [SD] WOMAC pain score = 2.8 [3.3]); lower scores (negative scores) indicate milder disease, while greater values indicate worse disease. The outcome was incident symptomatic knee OA (the combined state of frequent knee pain and radiographic OA [KLG≥2]) within three years after the disease activity measurement. We used logistic regression with repeated measures to assess the association between disease activity (continuous measure) and incident symptomatic knee OA, adjusting for gender, age, and body mass index.</div></div><div><h3>RESULTS</h3><div>Disease activity ranged from -3.3 to 31.1 (lower values = less effusion-synovitis and BM lesions). Knees that developed incident symptomatic knee OA had greater disease activity (-0.3 [2.7] vs. -1.1 [2.8]): the adjusted relative risk=1.06 (per 1 unit of disease activity; 95% confidence interval: 1.02-1.10). Our stratified analyses revealed those with only radiographic OA (adjusted relative risk=1.37 [1.06-1.78]) or only symptoms (adjusted relative risk=1.15 [1.03-1.28]) at baseline drove the associations between disease activity and incident symptomatic knee OA.</div></div><div><h3>CONCLUSION</h3><div>Our findings underscore the critical","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100306"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524131","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":"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":"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 , W. Wirth , A. Eitner","doi":"10.1016/j.ostima.2025.100286","DOIUrl":"10.1016/j.ostima.2025.100286","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>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.</div></div><div><h3>OBJECTIVE</h3><div>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.</div></div><div><h3>METHODS</h3><div>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.</div></div><div><h3>RESULTS</h3><div>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 , K.T. Gao , V. Pedoia , S. Majumdar , G.E. Gold , A.S. Chaudhari , A.A. Gatti","doi":"10.1016/j.ostima.2025.100330","DOIUrl":"10.1016/j.ostima.2025.100330","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>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.</div></div><div><h3>OBJECTIVE</h3><div>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.</div></div><div><h3>METHODS</h3><div>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).</div></div><div><h3>RESULTS</h3><div>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 < 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 < 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).</div></div><div><h3>CONCLUSION</h3><div>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 , M.A. van den Berg , N.S. Riedstra , M.M.A. van Buuren , J. Tang , H. Ahedi , N. Arden , S.M.A. Bierma-Zeinstra , C.G. Boer , F.M. Cicuttini , T.F. Cootes , K.M. Crossley , D.T. Felson , W.P. Gielis , J.J. Heerey , G. Jones , S. Kluzek , N.E. Lane , C. Lindner , J.A. Lynch , R. Agricola","doi":"10.1016/j.ostima.2025.100279","DOIUrl":"10.1016/j.ostima.2025.100279","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>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.</div></div><div><h3>OBJECTIVE</h3><div>To investigate the association between baseline demographics, RHOA, and automated, continuous JSW.</div></div><div><h3>METHODS</h3><div>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 < 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.</div></div><div><h3>RESULTS</h3><div>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<sup>2</sup>, 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}