Osteoarthritis imaging最新文献

{"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":"<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 &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.</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/m², 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":"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":"<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 ,&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":"<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 &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).</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":"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}

{"title":"ADVANCING HIP OSTEOARTHRITIS PREDICTION: INSIGHTS FROM MULTI-MODAL PREDICTIVE MODELING WITH INDIVIDUAL PARTICIPANT DATA OF THE WORLD COACH CONSORTIUM","authors":"M.A. van den Berg ,&nbsp;F. Boel ,&nbsp;M.M.A. van Buuren ,&nbsp;N.S. Riedstra ,&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.2025.100343","DOIUrl":"10.1016/j.ostima.2025.100343","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Radiographic hip osteoarthritis (RHOA) is a multifactorial disease, making early detection of individuals at risk challenging yet essential for timely intervention and evaluation of preventive strategies. Integrating information on multiple different data modalities using individual participant data from diverse cohorts may enhance predictive modeling in the early stages of RHOA. A focus on model interpretability may further enable the identification of clinically relevant patient subgroups and potential intervention targets.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;Creating a multi-modal prediction model for improving the performance of RHOA incidence prediction models compared to clinical features alone, and investigating the estimated predictor effects and the generalizability of the models to similar populations.&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 nine prospective cohort studies within the Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH consortium). All studies included standardized anteroposterior pelvic, long-limb, and/or hip radiographs, assessed for RHOA at baseline and after 4–8 years of follow-up. Incident RHOA was defined as the development of RHOA (grade ≥2) in hips without definite RHOA at baseline (grade &lt;2). The original cohort values of clinical predictors including age, birth-assigned sex, body mass index (BMI), smoking status, diabetes, and hip pain were harmonized into one consistent dataset. X-ray-derived predictors describing the hip morphology, the alpha angle and the lateral center edge angle, were automatically and uniformly determined using automated landmark points placed with Bonefinder®. Additionally, the values of 13 shape modes explaining 85% of the variation from a landmark-based statistical shape model were included. This SSM was built on all baseline RHOA grade &lt;2 hips within World COACH. Risk prediction models were built with generalized linear mixed effects models (GLMM) and Random Forest (RF) models while adjusting for correlations within cohorts and individuals. The discriminative performance (AUC) of different model configurations and the linear versus non-linear approaches were compared through stratified 5-fold cross-validation. For each model configuration, predictions were made with and without cohort labels to assess heterogeneity between cohorts.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;In total, 29,110 hips without definite RHOA at baseline were included of which 5.0% developed RHOA within 4-8 years (mean age 63.7 (8.6) years, 75.5% female, mean BMI 27.5 (4.7) kg/m&lt;sup&gt;2&lt;/sup&gt;). When comparing our uni-modal prediction model using only the clinical predictors (Model 1) to those with X-ray information added (Table 1), we observed a higher discriminative performance for the multi-modal models. Overall, including cohort information significantly improved model performance (p &lt; 0.05), and the RF mode","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100343"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523923","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":"PREVALENCE OF ACETABULAR DYSPLASIA IN 6-YEAR-OLDS IN A GENERAL POPULATION","authors":"N. Hendriks ,&nbsp;F. Boel ,&nbsp;C. Lindner ,&nbsp;F. Rivadeneira ,&nbsp;C.J. Tiderius ,&nbsp;S.M.A. Bierma-Zeinstra ,&nbsp;R. Agricola ,&nbsp;J. Runhaar","doi":"10.1016/j.ostima.2025.100292","DOIUrl":"10.1016/j.ostima.2025.100292","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Acetabular dysplasia (AD) is an important risk factor for early hip OA in adults. In Europe, infants are screened for developmental hip dysplasia. However, AD can also develop during skeletal maturation and these cases often remain unrecognized. Potentially, AD could be influenced prior to the closure of the hip growth plates. Understanding AD development during growth is crucial to prevent future joint degeneration. Different definitions are used to measure AD, depending on the stage of skeletal maturation. More knowledge of the prevalence of AD in the general population is required to understand its development during growth.</div></div><div><h3>OBJECTIVE</h3><div>1) To estimate the prevalence of AD in 6-year-olds from the general population, and 2) to compare different AD definitions in this age group.</div></div><div><h3>METHODS</h3><div>Data from The Generation R Study, a population-based study examining growth and health from fetal life to adulthood, was used. All participants aged 6 years, with high-resolution dual-energy x-ray absorptiometry (DXA) anteroposterior image of the right hip available were included. The hip shape was outlined with 70 landmarks using BoneFinder®. Using these landmarks, the acetabular index (AI), a measurement of acetabular roof inclination, was calculated to assess AD (AI&gt;20°). While AI is commonly used in children, the lateral center-edge angle (LCEA), as indicator for acetabular roof coverage of the femoral head, was also calculated. Mean LCEA and prevalence of AD (LCEA&lt;15°) were compared to measures using AI.</div></div><div><h3>RESULTS</h3><div>In total, 3,270 participants were included with a mean age of 6.2 (SD 0.6) years, and 51% was female. The mean AI was 11.3° (SD 5.0°) and the mean LCEA was 19.5° (SD 5.9°). The distribution for both AD definitions is shown in Figure 1. An AI&gt;20° was found in 124 participants, indicating a AD prevalence of 3.8% (95%CI, 3.1% - 4.5%). Based on the LCEA, the AD prevalence was 21.3% (95%CI, 19.9% - 22.7%).</div></div><div><h3>CONCLUSION</h3><div>The prevalence of AD in 6-year-olds is 3.8%, based on the AI. The LCEA classifies more hips as dysplastic in 6-year-olds. The validity of the LCEA in this age group and clinical relevance of these newly classified dysplastic hips need to be determined. A better understanding of the development of AD is important, as recovery during growth may be feasible and could contribute to the prevention of OA.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100292"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523990","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":"IN VIVO MICRO COMPUTED TOMOGRAPHY IMAGING ALLOWS LONGITUDINAL ASSESSMENT OF MULTI-SCALE CHANGES TO WHOLE JOINT WITH PROGRESSION OF OA","authors":"H. Liu,&nbsp;J.L. Gregory,&nbsp;M.O. Silva,&nbsp;C.E. Davey,&nbsp;K.S. Stok","doi":"10.1016/j.ostima.2025.100300","DOIUrl":"10.1016/j.ostima.2025.100300","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Longitudinal assessment of knee joint structure holds promise for providing invaluable spatial-temporal information on the progression of degenerative musculoskeletal (MSK) diseases involving the knee joint.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This proof-of-concept study aims to establish a time-lapse &lt;em&gt;in vivo&lt;/em&gt; imaging protocol with high temporal resolution to longitudinally track multi-scale structural changes, including mechanical alteration to whole joint structure, sensitive microstructural changes to subchondral bone, and abnormal bone remodeling activity, in a mouse collagenase-induced osteoarthritis (OA) model.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Eight male C57BL/10 mice aged nine weeks were recruited and assigned to two longitudinal groups, control (CT) and OA. Of these, four ten-week-old mice assigned to the OA group received intra-articular injection of collagenase on the right knee to destabilize the right tibiofemoral joint. Longitudinal &lt;em&gt;in vivo&lt;/em&gt; micro-computed tomography (microCT) scans were performed one day before collagenase injection and then weekly for eight weeks in total, resulting in nine scans for each animal. &lt;em&gt;In vivo&lt;/em&gt; microCT (Scanco Medical) was performed with a source voltage of 70 kVp, an integration time of 350 &lt;em&gt;ms&lt;/em&gt;, a current of 114 μ&lt;em&gt;A&lt;/em&gt;, and an isotropic nominal resolution of 10.4 μ&lt;em&gt;m&lt;/em&gt; with 1000 projections, with each scanning taking around 30 minutes. Quantitative morphometric analysis (QMA) was performed to measure longitudinal changes to structure of whole joint and subchondral bone, including joint space width (mm), and trabecular thickness (mm). Visualization of dynamic bone remodeling was performed by registering serial microCT scans. Bone resorption rate, BRR (%/day), and bone formation rate, BFR (%/day) were measured to quantify bone remodeling activity. To test the differences between CT and OA group at each time point from week 1 to week 8, a one-way analysis of covariance was used.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Three weeks post OA-induction, a significantly smaller joint space width was observed in medial osteoarthritic joint (202 μm), when compared to CT joint (228 μm) (p &lt; 0.01). Regarding trabecular thickness, significant differences were observed at multiple time points between CT and OA groups, specifically in the first three weeks at the early stage of OA progression at lateral side (p &lt; 0.01). Representative 3D visualization of bone formation and bone resorption is shown in &lt;strong&gt;Figure 1 A-B&lt;/strong&gt;. Abnormal bone remodeling activities were observed in osteoarthritic femur. When compared to control femur, significantly larger bone resorption rate was observed in the first week post collagenase injection in both the lateral (p &lt; 0.01) and medial femur (p &lt; 0.01), as shown in &lt;strong&gt;Figure 1 C-D&lt;/strong&gt;.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;This proof-of-concept study, for the first time, demonstr","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524185","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 FULLY-AUTOMATED TECHNIQUE FOR KNEE CARTILAGE AND DENUDED BONE AREA MORPHOMETRY IN SEVERE RADIOGRAPHIC KNEE OA – METHOD DEVELOPMENT AND VALIDATION","authors":"W. Wirth ,&nbsp;F. Eckstein","doi":"10.1016/j.ostima.2025.100349","DOIUrl":"10.1016/j.ostima.2025.100349","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Automated cartilage segmentation using convolutional neural networks (CNN) has been shown to provide moderate to high accuracy in comparison with gold-standard manual approaches. It also displays similar sensitivity to longitudinal change and to between-group differences in change as has been reported for manual analysis [1-3]. Denuded areas of subchondral bone (dAB) provide challenges and impair the accuracy of automated cartilage segmentation in knees with severe radiographic OA (KLG 4). The reason is that CNNs are trained to detect cartilage, but encounter “difficulties” to properly segment areas where cartilage is lost entirely. CNNs therefore often segment cartilage cover in some areas of actual full thickness loss or ignore dABs entirely. This was observed to result in an overestimation of cartilage thickness and an underestimation of dABs in knees with severe OA [4].&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To improve CNN-based automated segmentation in severely osteoarthritic knee cartilage by using an automated post-processing algorithm that relies on a multi-atlas registration for reconstructing the total area of subchondral bone (tAB). We evaluate the agreement, accuracy and longitudinal sensitivity to cartilage change of this new methodology.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Sagittal DESS and coronal FLASH MRIs were acquired by the Osteoarthritis Initiative (OAI). 2D U-Net models were trained for both MRI protocols using manual cartilage segmentations of knees with radiographic OA (KLG2-4, n training / validation set: 86/18 knees, baseline scans only) or severe radiographic OA (KLG4, n training/ validation set: 29/6 knees. These were trained either from baseline scans only [KLG4&lt;sub&gt;BL&lt;/sub&gt;] or from baseline and follow-up scans [KLG4&lt;sub&gt;BL+FU&lt;/sub&gt;]. The trained models were then applied to the test set comprising 10 KLG4 knees with manual cartilage segmentations from both DESS and FLASH MRI available and to n=125/14 knees with manual cartilage segmentations from either DESS or FLASH MRI available. Automated, registration-based post-processing was applied to reconstruct missing parts of the tAB and to refine the segmentations (Fig. 1), particularly in areas of denuded bone. The agreement and accuracy of automated cartilage analysis were evaluated in the test set for individual cartilages using Dice Similarity coefficients (DSC), correlation analysis, and by determining systematic offsets between manual and automated analysis. The sensitivity to one-year change was assessed using the standardized response mean (SRM) across the entire femorotibial joint in 104/24 (DESS/FLASH) knees with manual baseline and follow-up segmentations.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;The strongest agreement (DSC 0.80±0.07 to 0.89±0.05) and lowest systematic offsets for cartilage thickness (1.2% to 8.5%) were observed for CNNs trained on KLG2-4 rather than KLG4 knees. Similar observations were made for dABs (-40.6% to 3.","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100349"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522381","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":"BEYOND ACETABULAR DYSPLASIA AND PINCER MORPHOLOGY: REFINING HIP OSTEOARTHRITIS RISK ASSESSMENT THROUGH STATISTICAL SHAPE MODELING","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.2025.100341","DOIUrl":"10.1016/j.ostima.2025.100341","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Hip morphology has been recognized as an important risk factor for the development of hip OA. In previous studies within the Worldwide Collaboration on OsteoArthritis prediCtion for the Hip consortium (World COACH), both acetabular dysplasia (AD) and pincer morphology–characterized by acetabular under- and overcoverage of the femoral head–were associated with the development of radiographic hip OA (RHOA) within 4-8 years, with an odds ratio (OR) of 1.80 (95% confidence interval (CI) 1.40-2.34) and 1.50 (95% CI 1.05-2.15), respectively. However, we know that not everyone with AD or pincer morphology will develop RHOA. Specific baseline characteristics or variations in hip shape among individuals with AD and pincer morphology may influence their risk of developing RHOA. Statistical shape models (SSM), describing the mean hip shape of a population and a range of independent shape variations, can be utilized to study these variations in hip shape.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To evaluate whether specific hip shape variations or baseline characteristics within individuals with either AD or pincer morphology are associated with the development of RHOA within 4-8 years.&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 seven prospective cohort studies within the World COACH consortium. Standardized anteroposterior (AP) pelvic radiographs were obtained at baseline and within 4-8 years follow-up. RHOA was scored by KLG or (modified) Croft grade. We harmonized the RHOA scores into “No OA” (KLG/Croft = 0), “doubtful OA” (KLG/Croft = 1), or “definite OA” (KLG/Croft ≥ 2 or total hip replacement). The Wiberg center edge angle (WCEA), measuring the weight-bearing femoral head coverage, and the lateral center edge angle (LCEA), measuring the bony femoral head coverage, were automatically determined using a validated method. Hips were included if they had baseline and follow-up RHOA scores, no RHOA at baseline, and either AD defined by a WCEA ≤ 25° or pincer morphology defined by a LCEA ≥45°. For both populations, an SSM was created of the acetabular roof, posterior wall, femoral head and neck, and teardrop (Fig 1). We analyzed the first 13 shape modes that explained around 90% of total shape variation in the population. The association between each shape mode, sex, baseline age, BMI, diabetes and smoking habits, and the development of RHOA was estimated using univariate generalized linear mixed-effects models. The mixed effects were added to account for the potential clustering within cohorts and participants. The results were expressed as ORs with 95% CIs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;The AD population consisted of 4,737 hips, of which 2.6% developed incident RHOA (Table 1). Four of the 13 shape modes (Fig 1) were associated with the development of RHOA. Additionally, in hips with AD, females had higher odds of incident RHOA than males (OR 2.85, 95% CI 1.46 – 5.58), and each year inc","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100341"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522508","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}