Osteoarthritis imaging最新文献

{"title":"EFFECT OF LATERAL MENISCUS POSTERIOR ROOT TEARS ON CARTILAGE AND MENISCAL MECHANICS","authors":"J.S. Broberg, E. Hoptioncann, A. Kimbowa, A. Yung, K. Bale, I. Hacihaliloglu, P. Lodhia, D.R. Wilson","doi":"10.1016/j.ostima.2025.100353","DOIUrl":"10.1016/j.ostima.2025.100353","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Measuring cartilage and meniscal mechanics in loaded knees is essential to understanding the effects of lateral meniscus posterior root tears (LMPRTs) and the effectiveness of meniscal repair procedures that seek to protect the joint from degeneration. Studies have assessed mechanics with thin-film pressure sensors or finite element models, but their conclusions are limited by the invasiveness or inherent assumptions of the techniques employed. Ultra-high field MRI provides sufficient resolution to measure cartilage and meniscal mechanics during loading in a compatible loading device, without requiring disruption or simulation of the articulating joint surfaces. However, no studies have evaluated the impact of LMPRTs on the cartilage and meniscal mechanics in a human cadaveric knee using such a method.</div></div><div><h3>OBJECTIVE</h3><div>Test the hypothesis that LMPRTs increase femoral and tibial cartilage strain and meniscal extrusion.</div></div><div><h3>METHODS</h3><div>Six human knee lateral compartments (mean age 70 yrs) were tested. Anatomical alignment in full extension was maintained during preparation. The lateral meniscus and its roots, meniscotibial ligament, and attachment to the popliteus, as well as the ACL, were preserved. Specimens were placed in a novel pneumatic compression apparatus customized for use a 9.4T MRI scanner. Morphologic scans with a resolution of 0.06 × 0.12 × 0.4 mm were acquired before loading and after 2 hours of loading (Figure 1). The load applied was constant and equivalent to 48% body weight to simulate two-legged standing. An artificial LMPRT was then created, and specimens were left unloaded until testing the next day with the same protocol. Joint tissues were manually segmented for both intact and LMPRT conditions, in both loaded and unloaded states. Flattened cartilage profiles were generated to calculate cartilage strain in the axial direction, with negative strain indicating compression. The mean and maximum strains in the tibiofemoral contact area were determined in both the femoral and tibial cartilage. Meniscal extrusion was measured as the perpendicular distance between the external edge of the meniscus and the line bisecting the external edge of the tibial plateau and femoral condyle in the most anterior slice of the popliteus’ insertion. All measures were compared between conditions with paired Student’s t-tests with significance set to 0.05.</div></div><div><h3>RESULTS</h3><div>Maximum compressive strain in the tibiofemoral contact region of the femoral (p = 0.013) and tibial (p = 0.010) cartilage increased significantly after the LMPRT (Figure 2). The increase in mean compressive strain in the tibiofemoral contact region after the LMPRT was not significantly different for the femoral (p = 0.103) or tibial (p = 0.065) cartilage. Likewise, the increase in meniscal extrusion after the LMPRT was not significantly different (p = 0.143). Specimens with a greater incr","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100353"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524180","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":"CHARACTERIZING MENISCAL CALCIFICATIONS WITH PHOTON COUNTING-BASED DUAL-ENERGY COMPUTED TOMOGRAPHY","authors":"E. Nevanranta , V.-P. Karjalainen , M. Brix , I. Hellberg , A. Turkiewicz , B. Shakya , P. Önnerfjord , S. Ylisiurua , A. Sjögren , K. Elkhouly , V. Hughes , J. Tjörnstrand , S. Saarakkala , M. Englund , M.A.J. Finnilä","doi":"10.1016/j.ostima.2025.100303","DOIUrl":"10.1016/j.ostima.2025.100303","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Meniscal calcifications, including basic calcium phosphate (BCP) and calcium pyrophosphate (CPP), are commonly associated with OA and may disrupt meniscal function, contributing to joint degeneration. However, the role of specific calcification types in OA is not fully understood due to the lack of non-invasive imaging techniques that can differentiate them <em>in vivo</em>. While Raman spectroscopy accurately distinguishes BCP from CPP, it is limited to 2D and requires destructive histological processing. In contrast, dual-energy computed tomography (DECT) has shown potential for differentiating calcifications in both <em>in vivo</em> and <em>ex vivo</em>, but its performance varies across previous studies. The integration of photon-counting detectors (PCD) in CT imaging improves spatial resolution and enables multi-energy acquisition, enhancing <em>in vivo</em> calcification characterization.</div></div><div><h3>OBJECTIVE</h3><div>We evaluated the capability of dual-energy computed tomography with a photon counting detector (PCD-DECT) to differentiate BCP and CPP calcification deposits in the posterior horns of human menisci <em>ex vivo</em>, using Raman spectroscopy as the reference.</div></div><div><h3>METHODS</h3><div>This study included 82 medial and lateral meniscus samples from 21 deceased donors without known knee OA and 20 TKR patients with medial compartment OA. Samples were imaged using an experimental cone-beam CT setup with PCD, operating at 120 kVp and 0.2 mA. Low energy (LE) data were collected in the 20-50 keV range, and high energy (HE) data in the 50-120 keV range, with a final voxel size of 37 µm. Only calcified samples identified using Raman spectroscopy (n = 36), 8 CPP and 28 BCP samples, were included to the analysis. Calcifications were segmented and divided between BCP and CPP groups. Subsequently, LE, HE, and Dual Energy Index (DEI) values were measured for each calcification. We used linear mixed models to estimate associations between LE and HE variables and the calcification type, and to compare the DEI values between the calcification types. Estimates are presented with 95% confidence intervals.</div></div><div><h3>RESULTS</h3><div>Figure 1A-C shows a 3D visualization of menisci with and without different calcifications. The results showed that CPP calcifications had consistently lower LE values than BCP for corresponding HE values. The difference increased with higher HE values, peaking at 500 HU with a difference of 166.1 HU (95% CI: 73.4, 258.8), while the smallest difference occurs at -100 HU, where the difference is 33.81 HU (95% CI: -40.38, 107.99) HU. The differences between LE and HE values are shown in Figure 1D-E. Additionally, estimated mean DEI values were higher in BCP calcifications compared to CPP, with an estimated difference of 0.035 (95%CI: 0.011, 0.059). Detailed results are shown in Table 1.</div></div><div><h3>CONCLUSION</h3><div>Our findings show that BCP and CPP m","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100303"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524188","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":"AUTOMATED QUANTIFICATION OF MENISCUS EXTRUSION IN MRI VIA AI FOUNDATION MODEL: PROOF OF CONCEPT USING A TRAINING-FREE FEW-SHOT SEGMENTATION APPROACH","authors":"Z. Zhou , X. He , Y. Hu , H.A. Khan , F. Liu , M. Jarraya","doi":"10.1016/j.ostima.2025.100333","DOIUrl":"10.1016/j.ostima.2025.100333","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Manual assessment of meniscus extrusion (ME) in magnetic resonance (MR) images is time-consuming and prone to variability, limiting efficiency in clinical and research settings. While deep learning methods have shown promise in MR image segmentation, their reliance on task-specific training and large annotated datasets limits scalability and adaptability.</div></div><div><h3>OBJECTIVE</h3><div>Building upon our previously developed AI foundation model, we aim to establish a fully automated pipeline for quantifying ME in knee MRI with our model training and eliminate the need for large annotated datasets.</div></div><div><h3>METHODS</h3><div>By providing a support set including a minimal number of segmentation examples, the AI Foundation Model enables accurate segmentation of knee anatomy and reliable ME measurement in a training-free, few-shot manner. In the study, we analyzed 3T MR images acquired using either T2-weighted or proton density MR sequences from 10 patients with mild osteoarthritis. Manual segmentations of femur, tibia, medial, and lateral menisci were performed by experts. Two patients, one with T2-weighted and one with proton density images, were randomly selected to build the support set. The remaining 8 patients comprised the testing set, which was used for both automated segmentation and model evaluation. Segmentation performance was assessed using the Dice Coefficient. For ME evaluation, an experienced radiologist manually identified the slice containing the tibial spine and measured extrusion as the reference. Automated ME measurement was computed from the segmentation by detecting the femoral condyle and tibial plateau edge, then measuring the distance from the most medial point of the medial meniscus to a reference line connecting the femoral condyle and tibial plateau edge.</div></div><div><h3>RESULTS</h3><div>The average Dice Coefficient was 94.07 ± 3.97% for the femur, 97.09 ± 0.93% for the tibia, 82.91 ± 6.72% for the medial meniscus, and 85.49 ± 5.24% for the lateral meniscus. ME measurements predicted by the model were also compared with ground truth values. The human measured ME was 4.26 ± 1.46 mm, while the model-predicted ME was 4.18 ± 1.16 mm.</div></div><div><h3>CONCLUSION</h3><div>This study demonstrates that the foundation model enables reliable and fully automated quantification of meniscus extrusion from knee MR images without requiring training or large annotated datasets. With only two support examples, the model achieved accurate segmentation and ME measurement across eight testing subjects, underscoring its efficiency and strong generalization. Its consistent performance across key anatomical structures highlights its potential for expert-level evaluation in both clinical and research settings with minimal manual effort. Further work will explore semi-automated expansion of the support set and extension to diverse MRI protocols and osteoarthritis severities, and validation on","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100333"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524261","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":"REGIONAL VARIATION IN TRAPEZIOMETACARPAL BONE MICROARCHITECTURE IN FEMALES WITH OSTEOARTHRITIS USING HR-PQCT","authors":"M.T. Kuczynski , C. Hasselaar , G. Dhaliwal , C. Hiscox , N.J. White , S.L. Manske","doi":"10.1016/j.ostima.2025.100299","DOIUrl":"10.1016/j.ostima.2025.100299","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>The trapeziometacarpal (TMC) joint, comprised of the trapezium (TRP) and first metacarpal (MC1) bones, is a mechanically complex, saddle-shaped joint. Studies have estimated that the peak forces acting on the TMC joint are up to five times higher than the corresponding external forces [1]. Moreover, cadaveric studies have shown non-uniform cartilage loss in TMC joint with OA [2]. While several cadaveric studies have investigated TMC joint cartilage and bone changes, evaluation of subchondral bone changes in the TMC joint <em>in vivo</em> is lacking.</div></div><div><h3>OBJECTIVE</h3><div>The objective of this study was to investigate differences in bone microarchitecture in anatomical quadrants of the TMC joint in women with TMC OA compared to age- and sex-matched controls. We hypothesized that women with TMC OA will exhibit quadrant-specific differences in bone microarchitecture compared to controls. Specifically, we hypothesized that the volar region of the TMC joint will demonstrate an increase in trabecular thickness, bone volume, and volumetric bone mineral density due to localized bone adaptations as a response to increased loading in the volar region.</div></div><div><h3>METHODS</h3><div>14 females diagnosed with symptomatic TMC OA (mean age: 60 ± 6.5 years) and 12 similarly aged female controls (mean age: 59 ± 5.7 years) were scanned using HR-pQCT (XtremeCT2, Scanco Medical). A standard HR-pQCT scanning protocol was used (61 µm<sup>3</sup> voxels). Images were preprocessed using a Laplace-Hamming filter and segmented with a fixed threshold (15% of the maximum intensity). A bone coordinate system was automatically defined for the MC1 and TRP [3], and used to separate each bone into four anatomical quadrants: 1) radial-dorsal (RD), 2) radial-volar (RV), 3) ulnar-dorsal (UD), and 4) ulnar-volar (UV). For each whole bone and quadrant, we computed volumetric bone mineral density (vBMD, mg HA/cm<sup>3</sup>), bone volume fraction (BV/TV, %), and bone thickness (B.Th, mm). A mixed ANOVA was used to compare bone measures in each bone and quadrant between groups.</div></div><div><h3>RESULTS</h3><div>We did not observe a significant difference in total bone parameters between groups for the MC1 or TRP. However, we found a statistically significant interaction effect between the volar and dorsal quadrants of the TRP and group for B.Th (p = 0.02, Figure 1, Table 1). Compared to controls, the mean B.Th in the TRP of the OA group was 1.9% lower in the RD quadrant, 7.5% lower in the UD quadrant, 4.8% greater in the RV quadrant, and 6.2% greater in the UV quadrant.</div></div><div><h3>CONCLUSION</h3><div>Our results suggest that whole bone TMC microarchitecture may not differ between OA and controls; however, we found significant differences in quadrant bone microarchitecture. This suggests that the MC1 and TRP undergo localized bone microarchitectural changes to adapt to the loading of the TMC joint. Further, our results s","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100299"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523382","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":"CHANGES IN JOINT SPACE WIDTH ONE YEAR AFTER WEIGHT LOSS SURGERY","authors":"L.T. Vuononvirta , T.D. Turmezei , T.J. Frondelius , M.T. Nevalainen , S.J.O. Rytky , J.H. Määttä , S.A. Meriläinen , P.P. Lehenkari , M.A.J. Finnilä","doi":"10.1016/j.ostima.2025.100328","DOIUrl":"10.1016/j.ostima.2025.100328","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Weight loss—either conservative or surgical—can slow OA progression. Traditional radiography has limited sensitivity in detecting early or subtle joint changes induced either by OA or weight loss. The limitations of conventional radiography can be alleviated by using CT. Weight-bearing cone-beam CT -imaging can be used to detect the OA induced changes in JSW. These changes can be evaluated by using joint space mapping (JSM), a novel CT-based technique, which enables quantitative 3D assessment of JSW with high spatial resolution.</div></div><div><h3>OBJECTIVE</h3><div>To assess longitudinal changes in left knee joint space width following Roux-en-Y gastric bypass (RYGB) surgery using weight-bearing cone-beam CT, expecting that substantial weight loss would be associated with joint space widening.</div></div><div><h3>METHODS</h3><div>This one-year longitudinal study included 86 morbidly obese subjects (72 F, 14 M), of whom 45 underwent RYGB surgery and 41 served as a control group following conservative weight loss. Subgroup analyses were stratified by weight loss success. Unilateral weight-bearing cone-beam CT scans of the left knee in full extension were acquired at baseline and 1-year follow-up using Planmed Verity (voxel size: 0.2 mm³; 801 × 801 × 651; 96 kVp, 12 mA). Images were processed in DICOM format. JSM was performed using Stradview (v7.21) for segmentation and measurement of JSW, and WxRegSurf (v23) for co-registration to a canonical surface. Subregions were manually defined based on standard tibial cartilage thickness maps. Statistical parametric mapping (SPM) was conducted using MATLAB (2022) and SurfStat. Group differences in subregional mean JSW changes were assessed using two-sample t-tests.</div></div><div><h3>RESULTS</h3><div>In the control group, BMI change was 0.0 ± 5.8 kg/m². RYGB patients with successful weight loss (≥20% BMI reduction) showed a mean BMI change of −11.2 ± 8.4 kg/m², while the unsuccessful subgroup had a change of −5.7 ± 8.1 kg/m². Regarding JSW, both surgical subgroups exhibited a general trend toward joint space redistribution in the knee (Figure 1). However, subregional analysis revealed significant JSW widening (p < 0.05) in the successful group, particularly in the central region (+0.13 ± 0.05 mm) and the medial quarter (+0.22 ± 0.23 mm) of the medial tibiofemoral compartment (Figure 2). Non-significant narrowing was seen anteriorly in the lateral compartment along with posterior widening.</div></div><div><h3>CONCLUSION</h3><div>This study demonstrates that successful weight loss following RYGB surgery is associated with significant region-specific increases in JSW in the medial compartment of the knee at a one-year interval, specifically in the central and inner regions of the medial tibiofemoral compartment. These findings suggest that weight reduction may lead to unloading of joint structures, especially at the medial compartment, which may alleviate cartilage compress","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100328"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523430","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":"OPTIMIZED DEEP LEARNING METHOD FOR AUTOMATED SEGMENTATION OF BONE MARROW LESIONS","authors":"Q. Shihua , W. Qiong , S. Juan , B.D. Jeffrey , M. Timothy , Z. Ming","doi":"10.1016/j.ostima.2025.100319","DOIUrl":"10.1016/j.ostima.2025.100319","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Bone Marrow Lesions (BMLs), characterized by high-signal intensity on fat-suppressed MRIs, are associated with the progression of knee osteoarthritis (OA). In early OA or when joint damage is not visible on radiographs, BMLs are predictive markers for progression. However, their irregular distribution, potentially large size, and low-contrast boundaries challenge BML segmentation.</div></div><div><h3>OBJECTIVE</h3><div>This study introduces a novel training strategy for enhancing automated BML segmentation accuracy</div></div><div><h3>METHODS</h3><div>We aimed to optimize a deep learning method for automatic BML detection and segmentation in MRI, using the Osteoarthritis Initiative (OAI) dataset split into 70% training (210 participants), 15% validation (45 participants), and 15% testing (45 participants), totaling 1025, 190, and 201 MRIs, respectively. Images were employed using data augmentation like brightness, contrast, and geometric transformations. We applied a closing operation, a morphological technique combining dilation and erosion, to smooth edges, addressing the coarse manual labels that impair training. Several models (U-net, SwinUnetR, AttentionUnet, and U-net++) were trained with single-label (BML) and dual-label (BML + femur bone) outputs. Model performance was measured with the Dice Similarity Coefficient (DSC) for overlap and HD95 for boundary error. Cross-entropy and Dice loss functions improved sensitivity during training, particularly in dual-label channels where the femur bone location helped constrain BML positions. We also applied Pixel-Wise Voting (PWV) to improve segmentation stability and accuracy by averaging results from image variations, reducing false positives, and enhancing final segmentation outcomes.</div></div><div><h3>RESULTS</h3><div>UNet++ model with dual-label (BML + femur bone) yielded the best accuracy, outperforming U-net, SwinUnetR, and AttentionUnet. Figure 1 shows its predicted region (yellow) overlapping well with the manually labeled BML and aligning with boundaries. Specifically, the dual-label Unet model with PWV improved DSC from 62.21% to 64.88% for BML and to 96.52% for bone, while HD95 dropped to 26.82% for BML and 15.52% for bone. SwinUnetR with dual-label and PWV also showed improved DSC (65.06% to 66.70% for BML; 96.34% for bone) and reduced HD95 to 28.31% for BML and 11.54% for bone. AttentionUnet exhibited notable PWV improvements in bone segmentation. Overall, Unet++ achieved the highest performance with dual-label and PWV, increasing DSC from 66.16% to 68.48% for BML and 96.66% for bone, with the lowest HD95 values.</div></div><div><h3>CONCLUSION</h3><div>This study employed augmentation strategies, a closing operation, and both single- and dual-label analyses to train four models—Unet, SwinUnetR, AttentionUnet, and Unet++. Cross-entropy loss and Pixel-Wise Voting (PWV) enhanced model performance, with dual-label consistently outperforming single-label, es","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523454","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 THE CT OSTEOARTHRITIS KNEE SCORE (COAKS) AND A PROTOTYPE CT-GENERATED KELLGREN AND LAWRENCE GRADE","authors":"T.D. Turmezei , A. Boddu , Z. Akkaya , N.H. Degala , J.A. Lynch , N.A. Segal","doi":"10.1016/j.ostima.2025.100346","DOIUrl":"10.1016/j.ostima.2025.100346","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>The CT Osteoarthritis Knee Score (COAKS) is a semiquantitative system for grading structural disease features of knee OA from weight bearing CT (WBCT). Previous work has demonstrated substantial to near-perfect inter- and intra-observer reliability of COAKS with the aid of a feature scoring atlas, but test-retest repeatability has not yet been evaluated. Given that x-ray and CT rely on the same fundamental physical properties, COAKS could also be harnessed to provide a CT-generated KLG and avoid the need for radiographic imaging.</div></div><div><h3>OBJECTIVE</h3><div>(1) To evaluate test-retest repeatability of COAKS; (2) to develop a CT-generated KLG (ctKLG) and evaluate its test-retest repeatability; and (3) to compare this prototype ctKLG against radiographic KLG (rKLG).</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. Participant demographics were: mean ± SD age 61.3 ± 8.4 years, BMI 30.7 ± 4.3 kg/m<sup>2</sup> and male:female ratio 8:6. All scanning was performed on the same 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 anonymized prior to analysis both according to the individual and imaging attendance. All knees were reviewed for their 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 a custom MATLAB script to generate structural heat maps. Test-retest repeatability weighted Kappa (Kw) scores were calculated for each feature (J = JSW; O = osteophytes; C = subchondral cysts; S = subchondral sclerosis) at each compartment (MTF = medial tibiofemoral; LTF = lateral tibiofemoral; PF = patellofemoral; PTF = proximal tibiofibular). A custom MATLAB script applied a decision tree based on recognized KLG verbal definitions to generate ctKLGs for each knee, including a combined score for the MTF and LTF compartments to mimic single-view AP radiographic conditions. A second experienced musculoskeletal radiologist (Z.A.) read study inclusion radiographs for rKLG likewise blinded. Kw was also calculated for ctKLG and rKLG.</div></div><div><h3>RESULTS</h3><div>Structural heatmaps are shown in Figure 1 for participants with ctKLGs of 1 (study ID 117, right knee) and 4 (study ID 101, right knee) alongside difference maps (follow-up minus baseline). These maps give examples of minimal difference in baseline and follow-up grading at the extremes of structural dis","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100346"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523607","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":"TOPOGRAPHY OF SEX-RELATED FEMOROTIBIAL CARTILAGE THICKNESS DIFFERENCES: A MATCHED MALE-FEMALE PAIR ANALYSIS CONTROLLING FOR AGE, BMI, AND HEIGHT","authors":"N. Spoelder , W. Wirth , T.D. Turmezei , F. Eckstein , D.A. Kessler , J.W. Mackay , M. Karperien , S.C. Mastbergen , M.P. Jansen","doi":"10.1016/j.ostima.2025.100350","DOIUrl":"10.1016/j.ostima.2025.100350","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Knee OA is both more common and progresses faster in women than in men. While it is well known that men exhibit thicker cartilage, it remains unclear whether this difference is inherently sex-based or attributable to confounding factors such as age, BMI, and/or height.</div></div><div><h3>OBJECTIVE</h3><div>The aim of this study was to evaluate regional differences in knee cartilage thickness between men and women without radiographic OA, who were matched for age, BMI, and height.</div></div><div><h3>METHODS</h3><div>Participants without radiographic signs of knee OA were selected from the Osteoarthritis Initiative (OAI). Men and women were matched based on height (±1 cm), age (±5 years), and BMI (±2 kg/m²), yielding 63 male-female pairs (n = 126; mean age 57 ± 8 years, BMI 26 ± 4 kg/m², height 170 ± 5 cm). Right knee 3T MRI scans were processed using a deep learning model to generate preliminary automatic segmentations of the outer femoral and tibial contours and the inner cartilage boundaries. These segmentations were manually refined in Stradview and converted into 3D surface models. Cartilage thickness was computed at each vertex as the distance from the cartilage surface to the underlying bone, measured along the normal vector using model-based deconvolution. The femoral, medial tibial, and lateral tibial surfaces and their associated thickness maps were spatially aligned to canonical templates using wxRegSurf. Statistical analyses were performed in MATLAB using the SurfStat package, applying statistical parametric mapping (SPM) with linear mixed models to evaluate paired male-female differences. Significance was set at p < 0.05.</div></div><div><h3>RESULTS</h3><div>Figure 1 shows the average cartilage thickness in men and women, as well as the differences between sexes. The difference map is predominantly blue, indicating thicker cartilage in men. In both sexes, cartilage was thicker on the lateral side than on the medial side. The trochlea had the greatest thickness overall, with a maximum of 3.98 mm in men and 3.30 mm in women. Statistically significant differences in cartilage thickness between men and women were observed in specific regions of the femur, medial tibia, and lateral tibia (Figure 2). In those regions in the femur, cartilage was thicker in men, with a mean thickness of 2.77 mm compared to 2.42 mm in women, a difference of 0.36 mm (15%). In both the statistically significant different regions of the medial and lateral tibia, cartilage thickness was 0.09 mm (4%) greater in men than in women, with means of 2.26 mm versus 2.17 mm and 2.19 mm versus 2.10 mm, respectively.</div></div><div><h3>CONCLUSION</h3><div>Despite similar height, age, and BMI, men exhibited thicker femorotibial cartilage than women. Statistically significant differences were found across all three joint surfaces, with the largest difference observed in the trochlea. These findings underscore the need for further research in","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100350"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523628","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":"INCREASED BMI IS A MODIFIABLE RISK FACTOR OF SUBCHONDRAL INSUFFICENCY FRACTURE OF THE KNEE","authors":"M.Z. Alzaher , W. Issa , J. Husseini , A. Huang , A. Guermazi , M. Jarraya","doi":"10.1016/j.ostima.2025.100276","DOIUrl":"10.1016/j.ostima.2025.100276","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Subchondral insufficiency fractures of the knee (SIFK) are increasingly recognized as an important, but often underdiagnosed, cause of acute knee pain and functional decline, particularly in middle-aged and older adults. Existing studies on SIFK are often limited in number and lack a sufficient number of controls. Because of its rare occurrence in the general population and in major epidemiological studies, our understanding of the risk factors of SIFK remains limited. It has been hypothesized that excessive joint loading leads to focal stress concentrations within the subchondral bone plate, overwhelming its capacity for repair and ultimately predisposing to a microfracture. However, it remains unclear whether BMI is modifiable risk factor of SIFK.</div></div><div><h3>OBJECTIVE</h3><div>Our aim is to investigate the association between BMI and the occurrence of MRI-detected SIFK in a clinical setting.</div></div><div><h3>METHODS</h3><div>We conducted a case–control study at a tertiary academic hospital network from November 2022 to October 2024. Cases were identified using an institutional repository, based on MRI reports containing the diagnosis “subchondral insufficiency fracture”. The diagnosis of SIFK was confirmed by a MSK radiologist who reviewed all images. Matched controls were defined as patients within 5 years of age who underwent knee MRI for knee pain over the same period (±10 days) and who did not have subchondral insufficiency fracture on MRI (both in the MR report and after review of images). Electronic medical records were manually checked for the primary independent variable, BMI, and other variables such as age, sex, dyslipidemia, statin use, diabetes mellitus, and hypertension. Univariable and multivariable logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using SIFK as the primary dependent variable. Missing data on variables in the logistic regression model will be handled by listwise deletion.</div></div><div><h3>RESULTS</h3><div>The mean age (±SD) in years was 63.2 (±10.2) for the cases and 64.3 (±10.4) for the controls. Females made up 64% of the cases and 67% of the controls. Median (Q1–Q3) BMI was 29.5 kg/m<sup>2</sup> (26.0–34.4) for the cases and 27.1 kg/m<sup>2</sup> (23.7–31.4) for the controls. A two-sample t-test showed that BMI was significantly higher in the cases than in controls (p-value = 0.015). Univariable logistic regression with SIFK as the dependent variable and BMI as the independent variable estimated an OR of 1.06 (95% CI: 1.01–1.10). This statistically significant result remained after adjusting potential confounders such as sex, dyslipidemia, and statin use with an estimated OR of 1.05 (95% CI: 1.01–1.10) (<strong>figure 1 and table 1</strong>). <strong>Figure 2</strong> shows an example of SIFK.</div></div><div><h3>CONCLUSION</h3><div>Our preliminary results indicate that elevated BMI may be linked to greater odds of hav","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100276"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523631","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":"A FIRST-IN-HUMAN PHASE 1/2A CLINICAL STUDY OF ICM-203 AAV GENE THERAPY: PROMISING SIGNALS AS A DMOAD CANDIDATE","authors":"A. Heald , L. Bogdan Solomon , R. Page , Y.N. Yum , M. Park , J. Myung , J.E. Collins , A. Guermazi , D.W. Kim","doi":"10.1016/j.ostima.2025.100291","DOIUrl":"10.1016/j.ostima.2025.100291","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>ICM-203, a recombinant AAV vector designed to express a truncated form of human Nkx3.2, a transcription factor which plays an important role in both chondrocyte and synoviocyte activity, is in clinical development as a potential DMOAD.</div></div><div><h3>OBJECTIVE</h3><div>An unblinded interim analysis of the low dose cohort of the first-in-human phase 1/2a study of ICM-203 was conducted to assess the safety, immunogenicity, and biological activity of ICM-203.</div></div><div><h3>METHODS</h3><div>In the low dose cohort of this phase 1/2a, double-blind, placebo-controlled, dose escalation study (NCT04875754), 8 subjects with Kellgren-Lawrence grade 3 osteoarthritis (OA) of the knee were randomized to receive a single intra-articular injection of ICM-203 or placebo in a 3:1 ratio. The primary safety endpoint was safety and tolerability of ICM-203 through assessment of treatment-emergent adverse events (TEAEs). Immunogenicity endpoints included measuring serum neutralizing antibody (NAb) titers and T-cell responses to ICM-203’s AAV capsid. As efficacy endpoints, changes in knee pain and function were assessed by the Knee Injury and Osteoarthritis Outcome Score (KOOS) pain subscale and KOOS activities of daily living (ADL) subscale, respectively; these KOOS scores were converted to calculate Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores. Imaging endpoints included Magnetic Resonance Imaging (MRI) Osteoarthritis Knee Scores (MOAKS) focusing on bone marrow lesions (BML), synovitis, articular cartilage damage, and osteophytes.</div></div><div><h3>RESULTS</h3><div>Of 11 screened subjects, 8 qualified and received a single intra-articular injection of ICM-203 (N=6) or placebo (N=2); all subjects completed 52 weeks of follow-up. Subject age ranged from 56 to 73 years; body mass index (BMI) ranged from 24.6 to 38.6 kg/m2. No significant concerns about safety or tolerability arose. The most common treatment-related TEAE was mild to moderate arthralgia, which occurred in 3 of 6 ICM-203 subjects and 1 of 2 placebo subjects. At baseline, 3 ICM-203 subjects had positive NAb responses to AAV capsid; no subjects had significant T-cell responses. All 6 ICM-203 subjects developed both a humoral and cellular response against AAV capsid, whereas neither placebo subject did. ICM-203 subjects with negative NAb at baseline (N=3) demonstrated greater improvement over placebo subjects (N=2) in KOOS pain, KOOS ADL, WOMAC, as well as in imaging endpoints, including MOAKS BML and synovitis. For articular cartilage and osteophytes, no significant changes were observed in any subject between baseline and week 52.</div></div><div><h3>CONCLUSION</h3><div>Intra-articular injections of ICM-203 were safe and well tolerated. ICM-203 appeared to show greater therapeutic activity over placebo in subjects with negative NAb at baseline. Current findings indicate ICM-203 may demonstrate potential as a disease-modifying osteoa","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100291"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523989","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}