Osteoarthritis imaging最新文献

{"title":"NEW JSW MEASUREMENTS INCREASE RESPONSIVENSS TO CHANGE","authors":"J. Duryea","doi":"10.1016/j.ostima.2025.100285","DOIUrl":"10.1016/j.ostima.2025.100285","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Knee radiography is a low cost, convenient, and widely available modality for assessing KOA change longitudinally. Although seen on MRI, soft tissues such as cartilage and the meniscus are invisible radiographically and their change is measured indirectly as loss of radiographic JSW. This indirect association has the potential to reduce the responsiveness to change for JSW. JSW loss is likely due to a combination of cartilage and meniscus change but the level of contribution from each structure is not currently discernable from a radiograph.</div></div><div><h3>OBJECTIVE</h3><div>To develop and validate new measurements of JSW with improved responsiveness to change compared to the current method. We also hope this will begin to shed light on the individual contributions of cartilage and meniscus to JSW loss by systematically evaluating different JSW locations across the knee joint.</div></div><div><h3>METHODS</h3><div>We randomly placed all 4,796 OAI participants into either a training or testing group and selected all knees where fixed-location JSW (fJSW) was available at the x=0.15 to 0.3 (medial compartment) and x=0.7 (inner-most lateral compartment) locations at each of 6 follow-up time points (12, 24, 36, 48, 72, and 96 months). We defined a new JSW metric (JSW<sub>New</sub>) that was a linear combination of three individual fJSW measures:</div><div>JSW<sub>New</sub>= fJSW(x=0.25) + w<sub>1</sub> × fJSW(x=0.7) + w<sub>2</sub> × fJSW(x=x<sub>i</sub>),</div><div>where x<sub>i</sub> was one of 6 values in the medial compartment: 0.15, 0.175, 0.2, 0.225, 0.275 or 0.3; lower x<sub>i</sub> values corresponded to more peripheral locations. Using the training group, we varied w<sub>1</sub>, w<sub>2</sub> and x<sub>i</sub> to achieve the maximum responsiveness, defined as the magnitude of the standardized response mean (SRM) for baseline to the follow-up time point. Once optimized, the performance was evaluated using the independent testing set and compared in the test group to the SRM found for fJSW(x=0.25), which is generally considered the most responsive fixed location JSW. We performed separate optimization and testing for the 5 different baseline KL values and 6 distinct follow-up time points.</div></div><div><h3>RESULTS</h3><div>Table 1 summarizes the results. There is substantial improvement in the responsiveness (magnitude of the SRM values) for all follow-up time points and KL values. We did not observe a consistent pattern for the x<sub>i</sub> values other than the absence of x=0.15 (most peripheral) as an optimal value. w<sub>1</sub> was generally negative for KL4 knees suggesting that JSW<sub>New</sub> may be capturing pseudo-widening (seesaw effect) or possibly medial compartment meniscus extrusion for these knees. w<sub>2</sub>, the weight factor for the other medial compartment locations, was consistently positive although no discernable dependence on KL or follow-up time point was observed. Positive w<","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100285"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523413","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":"COMPROMISED TRABECULAR BONE OF THE KNEE IS A DOSE-DEPENDENT CORRELATE OF MORE SEVERE OSTEOPHYTES AND ADVANCED KLG","authors":"A.K.O. Wong , S. Costa , D. Jain , M.E. Hernandez , A. Cagnoni , S. Liu , V. Anwari , A. Naraghi , R. Mohankumar , J.D. Johnston , L. Giangregorio","doi":"10.1016/j.ostima.2025.100337","DOIUrl":"10.1016/j.ostima.2025.100337","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Previous studies have shown that bone turnover is elevated, and fracture risk is higher among knee osteoarthritis (KOA) patients, especially in later stages of disease. While there have been mixed findings with respect to areal bone mineral density (BMD)’s association with KOA severity, it remains unclear how volumetric bone morphometry at the knee is related to the development of radiographic disease features such as osteophytosis and attrition.</div></div><div><h3>OBJECTIVE</h3><div>It was hypothesized that having definite osteophytosis and attrition are each associated with compromised subchondral bone, including lower volumetric(v) BMD, apparent v.Tissue Mineral Density (vTMD) and a wider Tb.Sp.</div></div><div><h3>METHODS</h3><div>In this cross-sectional study, women 50-85 years old were recruited by convenience sample if they experienced knee pain ≥3 days a week, each lasting >3 hours, and if self-reported body mass index (BMI) was <30 kg/m<sup>2</sup>. On the knee with worse symptoms, they completed a peripheral quantitative CT (pQCT) knee scan, one slice (2.3±0.5mm, 200µm in-plane) prescribed per tibiofemoral compartment; and an anteroposterior knee X-ray for KLG, including breakdown semi-quantitative evaluation of osteophytosis, attrition, JSN, and sclerosis. pQCT knee images were analyzed using a previously reported iterative threshold-seeking algorithm (Tam et al. Skeletal Muscle 27(14) 2024) to separate trabecular bone from marrow. Apparent structural parameters were derived from bone volume, bone surface, and total volume according to equations by Parfitt’s model of parallel plates. General linear models examined how KLG and osteophyte score, and each of established (score > 2) KOA (KL), osteophytosis, and attrition were related to knee vBMD, vTMD, app: Tb.Sp, Tb.Th, Tb.N, and BV/TV. Models adjusted for age, BMI, use of pain medications, antiresorptives, glucocorticoids or intra-articular steroid injections.</div></div><div><h3>RESULTS</h3><div>Among 105 women (mean(SD) age: 62.6(9.0)yrs, BMI: 24.2(3.5)kg/m<sup>2</sup>, median KLG: 1(1,2), 41(39.1%) with established KOA), a higher KLG or established KOA were each associated with lower vBMD and vTMD (with effects larger for vTMD), and a larger app.Tb.Sp; though, only in advanced stage (KLG3/4) individuals (Table1). Attrition was only associated with larger Tb.Sp in the lateral femur. Having more advanced osteophytosis was dose-dependently linked to lower vBMD and larger app.Tb.Sp (Figure 1). These effects were only present at the femur and not the tibia, with magnitudes appearing larger in themedial compartment among moderate grade (score 2) knees, but dose-dependently only in the lateral compartment.</div></div><div><h3>CONCLUSION</h3><div>Among peri- to post-menopausal women without obesity, compromised bone characterized by lower apparent bone density and less intact trabecular structure, may be key correlates of having more advanced radiogra","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100337"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523932","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 NEW LENS ON SYNOVITIS: LABEL-FREE IMAGING OF WHOLE-MOUNT HUMAN PATHOLOGICAL SYNOVIAL MEMBRANE WITH MULTIPHOTON MICROSCOPY","authors":"M. Pradeep , S. Das Gupta , T. Zhang , T. Liimatainen , V.M. Pohjanen , P. Lehenkari , S. Palosaari , M. Finnilä","doi":"10.1016/j.ostima.2025.100308","DOIUrl":"10.1016/j.ostima.2025.100308","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>One of the typical hallmarks of osteoarthritis progression is the inflammation of the synovial membrane, also known as synovitis. Pathological synovitis assessment is usually performed with traditional 2D histopathology, which provides limited orientation-dependent information, requires chemical labeling, and is destructive in nature. Tissue clearing of the whole synovial biopsy and non-destructive optical sectioning using multiphoton microscopy (MPM) can overcome the limitations of 2D histological approaches. MPM offers high spatial resolution and utilizes the second harmonic signal (SHG) to provide specific information about collagen fibers. This study aims to establish a tissue-clearing approach to analyze pathological human synovial tissue using label-free MPM.</div></div><div><h3>OBJECTIVE</h3><div>The objectives of the study are: 1) to optimize a clearing-enabled label-free MPM protocol for synovial biopsies by comparing the clearing performance of a hydrophilic reagent (CUBIC protocol) and hydrophobic reagents Ethyl Cinnamate (ECi). 2) To quantitatively evaluate autofluorescence (AF) and SHG signals from synovium to understand synovial tissue morphology, cellularity, and fibrosis.</div></div><div><h3>METHODS</h3><div>For tissue-clearing protocol optimization, one synovial biopsy was cut into two sections. After formalin fixation, one section underwent CUBIC clearing protocol, and the other was dehydrated and immersed in ECi. For the MPM study, 12 synovial biopsies (6 OA, 6 rheumatoid arthritis [RA]) were formalin-fixed, dehydrated, and cleared with ECi solution. All samples were collected from total knee replacement surgeries at Oulu University Hospital, Finland. MPM was conducted using a 900 nm laser, capturing the SHG signal at 450 nm and the AF signal between 470–600 nm. A 16X/0.6 NA water-immersion objective was used for imaging, with a pixel size of 0.7 µm. At first, mosaics of the whole sample were acquired at depths of 600, 1000, and 1300 µm from the sample surface. Subsequently, Z-stack images (depth: 1mm; step size: 200 microns) of the AF channel that includes the lining layer were collected and used for 3D cell segmentation. Maximum intensity projections of the Z-stack were processed through intensity thresholding, binary masking, and watershed segmentation. Only particles with an area less than 500 µm² were considered individual cells. Moreover, adipocytes and vascularity within the sub-lining layer from the 2D mosaic images were manually identified. Further, the heat maps for SHG intensity and area fraction were calculated. Finally, the tissue clearing was reserved, and the standard histopathological assessment of synovitis (Krenn scoring system) was performed.</div></div><div><h3>RESULTS</h3><div>ECi clearing achieved complete transparency of a synovial biopsy in 3 days (cleared around 1.2 mm), while the CUBIC protocol was still partially opaque tissue even after 3 weeks (cleared around 500 µm), ","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100308"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524133","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":"PHASE II RCT OF LEVI-04, A NOVEL NEUROTROPHIN-3 INHIBITOR, IN PEOPLE WITH KNEE OSTEOARTHRITIS: IMAGING EXCLUSIONS DURING SCREENING","authors":"A. Guermazi , P.G. Conaghan , C.M. Perkins , C. Herholdt , I. Bombelka , S.L. Westbrook","doi":"10.1016/j.ostima.2025.100339","DOIUrl":"10.1016/j.ostima.2025.100339","url":null,"abstract":"<div><h3>Introduction</h3><div>LEVI-04 is a first-in-class fusion protein (p75NTR-Fc) that supplements the endogenous p75NTR binding protein, providing analgesia via inhibition of NT-3 activity. Like p75NTR, LEVI-04 binds all the neurotrophins (NTs) with differing affinities, with highest to NT-3 and lowest affinity and reversibly to NGF, distinguishing the LEVI-04 mechanism of action from that of anti-NGF antibodies. As serious joint adverse events were seen in the anti-NGF trials, rigorous surveillance of joint safety was performed in this study. In order to properly categorise the risk of adverse joint events with LEVI-04, participants with potentially confounding findings at screening were excluded. LEVI-04 was well tolerated, with no increased incidence of joint pathologies compared to placebo.<sup>1</sup></div></div><div><h3>Methods</h3><div>This was a phase II multicentre (Europe and Hong Kong) RCT in adults with knee OA. Participants were randomized to 4-weekly IV placebo or 0.3, 1, or 2 mg/kg LEVI-04 through week 16, with the final visit at week 20 and a telephone safety follow-up at week 30. Participants who met initial clinical inclusion criteria underwent X-rays of bilateral shoulders, hips and knees, and then MRI of both knees (in some cases, MRI was performed in parallel with X-rays). All images were read centrally and assessed for eligibility. At week 20, all X-rays were repeated, and MRI of the target knee was performed.</div></div><div><h3>Results</h3><div>1598 people with painful knees were screened and 518 participants enrolled. 1080 people (86%) did not proceed past screening. 345 people exited the study before X-rays were performed (151 due to not meeting initial minimum pain in at least one knee, others due to other entry criteria, or sponsor, investigator or participant decision), such that a total of 1253 participants had X-rays of the large joints (Table 1). 514 (41%) people had knee exclusion criteria on X-ray, however this included 207 (left) and 188 (right) knees of KL grade<2. Only one knee was required to have KL grade >2, resulting in 108 (8.6%) people failing on KL grade. Excessive malalignment and atrophic OA were the next highest criteria, with 43 (3.4%) and 42 (3.3%) failures respectively. 766 people proceeded to MRI of both knees. 234 (30.5%) of these failed, 168 (22.9%) due to meniscal root tear, and 42 (5.4%) due to subchondral insufficiency fracture. There were 7 (0.9%) cases of findings suggestive of primary or metastatic tumor detected on MRI and 1 (0.1%) on knee X-ray. 30 (2.4%) people were excluded on hip and 4 (0.3%) on shoulder X-rays. 5 hip and 24 knee joints had arthroplasty, but these were not exclusionary. Several people exhibited more than one pathology, so reasons for exclusion slightly exceed the total number of people excluded.</div></div><div><h3>Conclusion</h3><div>A significant proportion of people with OA show radiologic findings at screening. Excluding these patients is important to ","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100339"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PREDICTING KNEE OSTEOARTHRITIS PROGRESSION USING STRUCTURAL BIOMARKERS FROM MULTIPLE JOINTS: DATA FROM THE OSTEOARTHRITIS INITIATIVE","authors":"M. Raza , T. Laffaye , R. Stein , H. Ragati-Haghi , R. Amesbury , A. Mathiessen , C.K. Kwoh , J.E. Collins , J. Duryea","doi":"10.1016/j.ostima.2025.100310","DOIUrl":"10.1016/j.ostima.2025.100310","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Clinical risk prediction models have been developed to predict knee OA progression with the goal of targeted treatment and clinical trial enrichment. It remains unclear whether, or how, OA in other joints affects knee OA progression.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate whether imaging biomarkers from non-index joints add predictive value for knee OA progression beyond those from the index knee alone.</div></div><div><h3>METHODS</h3><div>We included 648 participants from the Osteoarthritis Initiative (OAI), randomly selected with baseline KL grade of 1, 2, or 3. OAI obtained bilateral knee and hip XR and index knee MRI. Baseline imaging biomarkers included quantitative measures of index and non-index knee and hip fixed location joint space width and femorotibial angle (FTA) from XR and quantitative measures of cartilage thickness from index knee MRI. Clinical covariates were age, sex, BMI, injury history, surgery history, family history of knee replacement, and clinical hand OA (based on presence of Heberden’s nodes at the baseline clinical examination). Outcomes were knee OA progression over 48 months defined as (1) decrease in medial minimum joint space width (JSW) of ≥ 0.7mm and (2) any increase in KL grade.</div><div>We used random forests to determine the combination of predictors that maximize AUC. Random forests can model complex non-linear associations, interactions among predictors, and work well in the setting of correlated data. We examined each set of biomarkers alone and in combination: clinical covariates, index knee XR, contralateral knee XR, index hip XR, contralateral hip XR, index knee MRI. Models were tuned with 5-fold cross-validation and AUCs were computed over 1000 bootstrap samples. We used permutation-based variable importance to rank the most important variables for prediction.</div></div><div><h3>RESULTS</h3><div>The 648 OAI participants were 23% KLG 1, 48% KLG 2, and 28% KLG 3. Average age was 61 (SD 9) and average BMI 29 (SD 5). 152 (23%) had a decrease in JSW ≥0.7mm and 119 (18%) had an increase in KL grade.</div><div>In considering sets of covariates on their own, models with index knee MRI had the highest AUC for both outcomes (model 8), followed by models with index knee XR (model 3, Table). Adding contralateral hip XR to models with index knee XR improved AUC. For example, in predicting JSW≥0.7mm, the AUC increased from 0.627 (model 9) to 0.648 (model 10). Adding hip XR biomarkers did not seem to improve model discrimination (model 10 to model 11). AUCs from models from hip XR biomarkers alone were modest, though higher than for models with only clinical covariates.</div><div>Variable importance for the 10 most important biomarkers for the model with all XR biomarkers (model 12) is shown in the Figure for JSW ≥0.7mm (panel A) and KLG increase (panel B). Baseline medial minimum JSW was the most important predictor for both models. Various measures of fixed location JSW i","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE AGING JOINT: QUANTITATIVE [18F]NAF PET-MR IMAGING OF CELLULAR & MOLECULAR CHANGES IN BONE, CARTILAGE AND MUSCLE ACROSS THE LIFESPAN","authors":"A. Goyal, Y. Vainberg, F. Belibi, A.A. Gatti, M.S. White, R. Shalit, F. Kogan","doi":"10.1016/j.ostima.2025.100334","DOIUrl":"10.1016/j.ostima.2025.100334","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Osteoarthritis (OA) is increasingly recognized as a whole-joint disease, affecting cartilage, subchondral bone and periarticular muscles. While structural changes throughout the lifespan have been investigated in prior work, few studies have explored early cellular and molecular changes, such as bone metabolism, cartilage matrix composition, and muscle quality. In this study, we simultaneously assessed bone metabolic activity, cartilage microstructure, and muscle morphometry and composition in vivo, and examined their associations with key OA risk factors including age, body mass index (BMI), and sex.</div></div><div><h3>OBJECTIVE</h3><div>To characterize cellular and molecular features of bone, cartilage, and muscle in asymptomatic adults, and determine how these metrics vary with key OA risk factors of age, BMI, and sex.</div></div><div><h3>METHODS</h3><div>Forty-five asymptomatic subjects (23-79 years old, 22 female) with no history of knee injury or symptomatic arthritis underwent bilateral knee imaging on a 3T GE PET-MRI scanner (Figure 1). Quantitative DESS MR images (TEs 6 and 30.4 ms) were used to compute mean cartilage T2 relaxation time and thickness in femoral, tibial and patellar subregions, which were segmented using a previously validated automated pipeline. Dynamic [<sup>18</sup>F]NaF PET scans were acquired before and after a stair-climbing exercise (2.5mCi dose/injection) and were used to quantify Standardized Uptake Value measures (SUVmean, SUVmax) and their exercise-induced change: ΔSUVmean, ΔSUVmax. Iterative Decomposition of water and fat with Echo Assymetry and Least squares estimation (IDEAL) scans of the bilateral thighs were also acquired. The quadriceps, hamstrings, and hip adductors were segmented using an automated pipeline (MuscleMap) and muscle volume (normalized to BMI), fat fraction, and lean muscle mass were calculated for each muscle. Statistical analysis included a linear mixed effects model for each tissue outcome (cartilage, bone, and muscle metrics), where sex (male vs. female), age (years) and BMI (kg/m²) were included as fixed-effect predictors, and random intercepts for subject and for side nested within subject (to account for the paired left/right measures) captured within‐individual correlation. Significance threshold was set at p < 0.05 for this analysis.</div></div><div><h3>RESULTS</h3><div>Table 1 shows results from the linear mixed effects model.</div><div>1) Higher BMI was associated with markedly greater baseline (SUVmean and SUVmax) and post‐exercise bone tracer uptake (ΔSUVmean and ΔSUVmax), indicating increased bone turnover in individuals with higher body mass. Age was linked specifically to higher maximum uptake measures (SUVmax and ΔSUVmax), suggesting that focal sites of remodeling intensify with aging even if the overall mean uptake remains relatively stable.</div><div>2) In cartilage, T2 relaxation times rose progressively across whole, deep, and superfic","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100334"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524179","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":"EXPLORING SEX-BASED HIP MORPHOLOGY DIFFERENCES IN YOUNG ADULTS USING AN AUTOMATED 3D METHOD","authors":"M.A. Kamphuis ,&nbsp;E.H.G. Oei ,&nbsp;J. Runhaar ,&nbsp;D.F. Hanff ,&nbsp;J.J. Tolk ,&nbsp;R. Agricola ,&nbsp;S.M.A. Bierma-Zeinstra ,&nbsp;S. Klein ,&nbsp;J. Hirvasniemi","doi":"10.1016/j.ostima.2025.100295","DOIUrl":"10.1016/j.ostima.2025.100295","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Although many factors contribute to the development of hip OA, structural abnormalities such as acetabular dysplasia are recognized as contributing to early degenerative changes. To identify these abnormalities, conventional two-dimensional radiographic methods are still widely used to assess hip morphology. However, they inherently oversimplify the joint’s complex three-dimensional anatomy and are subject to limitations such as patient positioning.</div></div><div><h3>OBJECTIVE</h3><div>This study aimed to develop and validate an automated method for 3D hip morphology analysis, incorporating segmentation and image feature extraction, as well as to assess morphological differences between sexes.</div></div><div><h3>METHODS</h3><div>We analyzed data from 2454 participants from Generation R Cohort (mean ± standard deviation age and BMI: 18.4±0.6 years and 22.7±3.8 kg/m²) comprising 1199 males and 1255 females. Hip structures (femoral bone, acetabular bone, femoral cartilage, and acetabular cartilage) were automatically segmented from MRI. An nnU-Net ensemble model was trained on 40 manually segmented hips and its performance evaluated using the Dice score. From the segmentations, five categories of morphological features were computed: basic geometric metrics (centers and radii), cartilage volumes, angular measurements (tilt, version, neck shaft angle and coverage angles, alpha angles), coverage metrics, and joint space width measurements. Independent samples t-tests were used to evaluate sex -based differences.</div></div><div><h3>RESULTS</h3><div>The automatic deep learning segmentation model achieved mean ± standard deviation Dice scores of 0.97±0.004, 0.90±0.01, 0.76±0.02, and 0.77±0.02 for femoral bone, acetabular bone, femoral cartilage, and acetabular cartilage, respectively, on a hold-out test set of 10 hips. All biomarkers showed statistically significant differences (p&lt;0.05), we highlight those with the largest differences between male and female group means. Compared to males, females had smaller femoral and acetabular radii, as well as reduced cartilage volumes in both the femoral and acetabular regions (Table 1). Alpha angles were lower in females, particularly in the coronal plane, but also in the axial plane, while the acetabular version angle for females was greater (Table 1).</div></div><div><h3>CONCLUSION</h3><div>This study demonstrates the feasibility of automated 3D analysis for comprehensive hip morphology assessment. Overall, the analysis reveals consistent and measurable differences in hip morphology between sexes. These morphological insights may help clarify structural risk factors for early hip OA.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100295"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524183","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":"OSTEOARTHRITIS IS A MULTI-JOINT DISEASE. OR IS IT?","authors":"M.A. van den Berg ,&nbsp;E. Panfilov ,&nbsp;J.H. Krijthe ,&nbsp;R. Agricola ,&nbsp;A. Tiulpin","doi":"10.1016/j.ostima.2025.100326","DOIUrl":"10.1016/j.ostima.2025.100326","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;OA frequently affects both the hip and knee joints; however, most prognostic studies evaluate these joints in isolation. Given the biomechanical and systemic connections between them, this joint-specific focus may overlook important patterns of disease progression. A better understanding of combined hip and knee OA progression could support the development of more accurate prediction models and treatment strategies.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To determine whether combined hip and knee OA progression exhibits a distinct phenotype compared to isolated OA progression.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;This study used the OAI data, which comprises data from participants aged 45–79 years at risk of developing knee OA. The dataset features bilateral posteroanterior knee radiographs and standardized weight-bearing anteroposterior pelvic radiographs obtained at the baseline and 48-month follow-up visits. Minimal JSW (mJSW) was measured manually for knees and automatically for hips. OA progression was defined as JSN of ≥0.5 mm in either hip or ≥0.7 mm in either knee. Participants with no JSN in any of the four joints at the 48-month follow-up were excluded. The selected participants were classified as having isolated (either hip or knee) or combined (both hip and knee) progression. A logistic regression model incorporating clinical and structural baseline features was used to explore associations with combined progression compared to isolated progression. Baseline radiographic OA (ROA) status of each of the four joints was classified as no ROA (0), early ROA (1) and definite ROA (2) based on the KLG and modified Croft grades. Adjusted odds ratios (aOR) and their 95% confidence intervals, estimated using bootstrapping with 10,000 iterations, and the goodness-of-fit of the model were assessed.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Among the 1,190 included participants with any ROA progression (mean age 61.5 ± 8.8 years; BMI 29.1 ± 4.5; 55.1% female), 281 (23.6%) showed combined ROA progression. The co-occurrence of baseline hip and knee ROA grades was reviewed descriptively (Table 1). The observed relatively small prevalence of combined progression in this population prevented including these ROA status interaction effects within our model. The logistic model showed improved fit over an intercept-only model (likelihood ratio test, p &lt; 0.0001), and acceptable goodness-of-fit (Hosmer-Lemeshow test, p = 0.40). Several baseline features were associated with higher odds of combined progression compared to isolated, including older age, female sex, varus knee alignment in the right knee, higher mJSW in the hip, and having definite ROA in the left hip (Figure 1). Interestingly, having ROA in the right hip or valgus knee alignment in the left knee would decrease the odds of combined progression.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;Our findings suggest that combined hip and knee OA progression may represent a disti","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100326"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523429","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":"IMPROVED DCE-MRI OF OA SYNOVITIS IN THE PRESENCE OF EFFUSION","authors":"J.C. Waterton ,&nbsp;J.H. Naish ,&nbsp;M. Tibiletti ,&nbsp;L. Edwards ,&nbsp;M.J. Heaton ,&nbsp;J.D. Kaggie ,&nbsp;M.J. Graves ,&nbsp;R.J. Janiczek ,&nbsp;A. McCaskie ,&nbsp;F.J. Gilbert ,&nbsp;G.J.M. Parker ,&nbsp;J.W. MacKay","doi":"10.1016/j.ostima.2025.100329","DOIUrl":"10.1016/j.ostima.2025.100329","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Synovitis is increasingly important in OA, both for disease understanding and as a therapeutic target. Dynamic contrast-enhanced (DCE) MRI is a powerful tool providing regional pharmacodynamic biomarkers. Investigators commonly map synovitis using compartmental models, such as the Extended Tofts model (ETM) originally developed for neuroscience and oncology (1). ETM assumes the extravascular extracellular space (v_e) is a well-mixed compartment, an assumption commonly violated in the presence of effusion. Use of an unsuitable compartmental model sometimes produces physiologically implausible imaging biomarkers which lack face validity and damage confidence in the interpretation of any changes.</div></div><div><h3>OBJECTIVE</h3><div>1) to develop a 3-compartment model (3CM) suitable for DCE-MRI in OA in the presence of effusion; 2) to characterize the model by simulation; 3) to compare performance of new 3CM and conventional ETM in an OA study with between- and within-subject comparison.</div></div><div><h3>METHODS</h3><div>The model (2) (figure 1A), includes v_e in exchange with a well-mixed vascular plasma compartment v_p, and also with a third effusion-like compartment receiving contrast from, but not returning it to, v_e. It has previously been characterized in an RA setting (2). A previously-reported (3) knee OA DCE-MRI study includes 61 datasets from 21 subjects (6 healthy, 11 KL2, 4 KL3) imaged on multiple occasions, all segmented by a musculoskeletal radiologist (JWM). Data were fit voxelwise in VoxelFlow (Bioxydyn) using 3CM, ETM, and a Patlak-type uptake-only model (UOM). Akaike Information Criterion (AIC) was used to determine which model each voxel preferred. Between-subject means±SD and between-scan repeatability coefficients of variation (CoV) were determined for each biomarker, and also for the AIC-imposed parcellations.</div></div><div><h3>RESULTS</h3><div>In simulations when the generative model was 3CM, ETM performed poorly (except at low k₁), but when the generative model was ETM, 3CM performed almost as well as the generative model across the whole parameter space. In OA subjects (Figure 1B, Table 1), extreme unphysiologic values of v_e (red in Figure 1C) were seen with ETM but not 3CM, while repeatability CoV did not deteriorate for the new 3CM k₁ in comparison to conventional ETM (Table 1). Differences between healthy and OA subjects were preserved.</div></div><div><h3>CONCLUSION</h3><div>The new 3CM model provides plausible biomarker values and informative maps, avoiding unphysiologic parameter estimates. This offers drug developers greater confidence in interpreting drug-induced pharmacodynamic responses.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100329"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523431","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":"AUTOMATIC MENISCUS ANALYSIS DEMONSTRATES REPAIR IS NOT SUPERIOR TO MENISCECTOMY IN IMPROVING MENISCAL UTE-T2* PROPERTIES 2-YEARS POST ACLR","authors":"A.A. Gatti ,&nbsp;A.A. Williams ,&nbsp;C.R. Chu","doi":"10.1016/j.ostima.2025.100347","DOIUrl":"10.1016/j.ostima.2025.100347","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Concomitant meniscus tear is common with ACL injury and amplifies OA risk. MRI ultrashort echo-time T2* (UTE-T2*) is sensitive to the compositional integrity of the meniscus and is histologically verified to associate with collagen fibril alignment. We implemented an automated pipeline to determine whether meniscal T2* composition 2-years after ACL reconstruction (ACLR) differs between patients with and without a meniscal tear at the time of surgery.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To test whether menisci found to be torn at the time of ACLR exhibit, at 2-year follow-up, higher mean UTE-T2* reflecting greater compositional degeneration than intact menisci and whether meniscal repair demonstrates lower UTE-T2* than meniscectomy at 2-year follow-up.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;111 ACLR patients (53/111(48%) female; mean[SD] age: 32[10]yrs; BMI: 25[3]kg/m2) underwent 3T MRI 2 years after ACLR (2.0[0.9]years). UTE-T2* maps were generated by fitting a mono-exponential decay curve to sagittal T2*-weighted images using a Levenberg-Marquardt algorithm. Images were acquired at 8 TEs (32μs -16ms, non-uniform spacing) using a radial-out 3D Cones acquisition, TR = 22ms, in-plane resolution = 0.313 to 0.364 mm, and 3mm slice thickness. Menisci were automatically segmented using a U-Net pre-trained on &gt;300 DESS volumes and fine-tuned to segment root-sum-of-squares images combining Cones echoes 2–6. Training labels were generated by registering DESS images to Cones and propagating the segmentation. Automated segmentation was evaluated in a validation cohort using the dice similarity coefficient (DSC) and average symmetric surface distance (ASSD). The menisci were subdivided into anterior, middle, and posterior thirds using an automated polar coordinate-based system (Fig 1). Meniscal tear and treatment at the time of ACLR was assessed from operative reports. UTE-T2* differences between torn and intact menisci, and between repair versus meniscectomy were assessed with t-tests (or Mann-Whitney U tests). Statistical analyses were performed with SPSS (IBM) and Excel (Microsoft).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Automated segmentation in the validation cohort (n=16) had median DSC = 0.71 and ASSD = 0.52 mm for the medial, and DSC = 0.68 and ASSD = 0.51 mm for the lateral meniscus. At the time of ACLR, meniscal tears were observed in 56/111(50%) patients: 24/111(22%) of medial and 45/111(41%) of lateral menisci. More tears were resected: 11/24(45%) medial, 25/45(56%) lateral than repaired: 10/24(42%) medial, 15/45(33%) lateral. Patients with any medial meniscal tear had higher mean UTE-T2* in the middle (14%, p&lt;0.001) and posterior (20%, p=0.002) regions compared to those with intact menisci, (Fig 2). Patients with any lateral meniscal tear had 20% higher mean UTE-T2* in the middle region of the lateral menisci compared to those with intact menisci (p=0.001). Two years post-ACLR, no mean UTE-T2* differences were","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 ","pages":"Article 100347"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523608","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}