Osteoarthritis imaging最新文献

{"title":"Committee Members","authors":"","doi":"10.1016/S2772-6541(26)00069-3","DOIUrl":"10.1016/S2772-6541(26)00069-3","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100459"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conference Timetable","authors":"","doi":"10.1016/S2772-6541(26)00070-X","DOIUrl":"10.1016/S2772-6541(26)00070-X","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100460"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COMPARATIVE ANALYSIS OF 3D CNN, NNU-NET, AND SAM2-BASED MODELS FOR KNEE MRI SEGMENTATION","authors":"N. Machlovi, D. Arsoy, C.M. Deniz, O. Cigdem","doi":"10.1016/j.ostima.2026.100435","DOIUrl":"10.1016/j.ostima.2026.100435","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>MRI enables assessment of morphological and compositional changes in knee cartilage and meniscus relevant to osteoarthritis (OA) progression. Accurate quantification depends on reliable segmentation, which remains challenging due to the thin, curved geometry of cartilage and complex shape of the meniscus. Manual delineation is time-consuming and impractical for large-scale studies. Deep learning models, including nnU-Net and transformer-based foundation models such as MedSAM2, have shown promise for automated segmentation; however, the effectiveness of fine-tuning MedSAM2 for multi-structure knee segmentation and its performance relative to nnU-Net on established benchmarks such as the IWOAI Challenge has not been reported.</div></div><div><h3>OBJECTIVE</h3><div>1) To evaluate and compare the performance of domain-specific fine-tuned MedSAM2 and nnU-Net for automated multi-structure knee MRI segmentation using all OAI baseline cohort. 2) To compare the segmentation performance of both models on the IWOAI Knee Segmentation Challenge dataset.</div></div><div><h3>METHODS</h3><div>Sagittal DESS knee MR images from the OAI baseline dataset (n = 9,592 MRI of left and right knees) and corresponding segmentation masks were used. Data was split at the subject level using stratified sampling across KL grades into 70/10/20 training, validation, and test sets. MedSAM2 was fine-tuned for 75 epochs, resampling sagittal slices to 512 × 512 resolution, using a combined Focal and Dice loss (20:1 weighting). Prompts were derived from the 25th, 50th, and 75th percentile slices of each 3D volume, with ground truth masks from these slices passed as initial prompts to propagate segmentation to the rest of the slices. For the IWOAI Challenge evaluation, the same data splits of the challenge (n = 120 training, 28 validation, 28 test) were used for fine-tuning both MedSAM2 and 3D nnU-Net. Performance was assessed using Dice score and IoU.</div></div><div><h3>RESULTS</h3><div>Using the entire OAI baseline dataset (Objective 1), KneeSAM2 achieved a mean Dice of 0.92 and IoU of 0.88, surpassing pretrained MedSAM2 by 0.20 in Dice and 0.23 in IoU. Improvements were most pronounced for smaller structures: patellar cartilage (Dice 0.90 vs. 0.41), meniscus (0.90 vs. 0.48), and tibial cartilage (0.89 vs. 0.64). Using the IWOAI Challenge dataset (Objective 2), MedSAM2-FT outperformed the published IWOAI benchmark for patellar cartilage (Dice 0.88 vs. 0.86) and matched it for femoral cartilage (0.90 vs. 0.90) but showed lower performance for tibial cartilage (0.75 vs. 0.89) and meniscus (0.85 vs. 0.88). The nnU-Net model, trained on the same IWOAI data splits, achieved the highest performance overall (Dice: femoral cartilage 0.95, tibial cartilage 0.95, patellar cartilage 0.93, meniscus 0.94), surpassing all other models.</div></div><div><h3>CONCLUSION</h3><div>Domain-specific fine-tuning improved MedSAM2 performance for segmenting small and anatomica","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100435"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PROGNOSTIC VALIDITY AND SENSITIVITY TO CHANGE OF AUTOMATICALLY MEASURED HOFFA RADIOMICS IN PROGRESSOR VS. NON-PROGRESSOR KNEES","authors":"W. Wirth, T. Winkler, J. Collins, D.J. Hunter, F.W. Roemer, A. Guermazi, F. Eckstein","doi":"10.1016/j.ostima.2026.100446","DOIUrl":"10.1016/j.ostima.2026.100446","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>High signal intensity alterations on non-enhanced fluid-sensitive fat suppressed MRI can be observed in the intracapsular and extrasynovial Hoffa’s fat pad. These are commonly used as a proxy for whole-joint inflammation in studies lacking direct synovitis visualization using contrast-enhancement (CE). Radiomics extract mathematical constructs from imaging data that are not accessible to the human eye; these have been shown to increase the prediction of predefined outcomes, particularly in the field of oncology.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate the cross-sectional and longitudinal prognostic validity of Hoffa radiomics for predicting OA progression and their sensitivity to longitudinal change over two years.</div></div><div><h3>METHODS</h3><div>We analyzed the OAI FNIH-1 data: 194 knees with combined radiographic and pain progression (ComP), 103 with radiographic-only (ROP), 103 with pain-only (POP), and 200 without progression (NonP). Hoffa’s fat pad was automatically segmented using sagittal IW TSE MRIs of all 600 knees at baseline (BL), year 1 (Y1), and year 2 (Y2), using a U-Net trained on 160 manually segmented knees. Seventy-five texture and 19 signal intensity measures were extracted across all MRI slices covering Hoffa’s fat pad, focusing on its posterior 10%, i.e., the region adjacent to the synovial membrane (Fig. 1). A multi-component predictor, termed “quantitative progression probability” (QPP), was developed using stepwise logistic regression, to distinguish ComP from NonP. This was based on measures obtained at the intermediate Y1 visit, allowing unbiased application to BL, Y2, and to BL→Y2 change. The most sensitive texture and signal intensity features were identified across BL, Y2, and BL→Y2 based on Cohen’s d between ComP and NonP. Longitudinal change and standardized response mean (SRM) of the most sensitive texture and signal intensity measures between BL and Y2 were assessed. Odds ratios (ORs, normalized to the SD) for ComP, ROP, and POP (each vs. NonP) were estimated for these measures and for QPP using logistic regression, adjusted for baseline KLG, minimum JSW, pain medication use, WOMAC pain, age, sex, BMI, and race.</div></div><div><h3>RESULTS</h3><div>The\tQPP was based on two gray-level dependence matrix (GLDM) measures (GLDM variance and dependence entropy). The measures most sensitive to differences between ComP and NonP were the interquartile range of the signal intensity (SI-IQR) and the zone entropy of the gray-level size zone matrix (ZE-GLSZM). All three measures predicted ComP vs. NonP similarly at both BL and Y2 (ORs 1.5 to 2.2, Table 1). QPP and SI-IQR also predicted ROP vs. NonP at both visits (ORs 1.3 to 1.8), whereas ZE-GLSZM predicted ROP vs. NonP only at Y2 (OR 1.7, Table 1). Hoffa radiomic measures were not prognostic of POP vs. NonP (Table 1). ORs were somewhat smaller in longitudinal than in cross-sectional analyses, with the strongest ORs for BL→Y2 chan","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100446"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"INFRAPATELLAR FAT PAD MRI RADIOMICS AND DELTA-RADIOMICS FOR PREDICTING PAIN PROGRESSION IN KNEE OSTEOARTHTRITIS","authors":"M. Zhang,&nbsp;Q. Dang,&nbsp;Z. Wang,&nbsp;C. Ding","doi":"10.1016/j.ostima.2026.100452","DOIUrl":"10.1016/j.ostima.2026.100452","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Knee osteoarthritis (KOA) pain is often discordant with structural abnormalities on conventional imaging, limiting the ability of routine magnetic resonance imaging (MRI) to predict symptom progression. The infrapatellar fat pad (IPFP), a metabolically active intra-articular tissue implicated in local inflammation, may provide quantitative imaging biomarkers relevant to pain progression.</div></div><div><h3>OBJECTIVE</h3><div>To determine whether baseline IPFP MRI radiomics and two-year IPFP delta-radiomics can predict pain progression in KOA and whether their performance exceeds that of clinical features and whole-joint MRI Osteoarthritis Knee Score (MOAKS) models.</div></div><div><h3>METHODS</h3><div>We conducted a retrospective cohort study using 1,822 knee MRI scans from the Osteoarthritis Initiative. Radiomic features were extracted from the IPFP on knee MRI. A baseline IPFP radiomics model and a longitudinal two-year delta-radiomics model were developed to predict KOA pain progression. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) in internal validation and external test sets. Predictive performance was compared with models based on clinical features and MOAKS. Multivariable regression was used to evaluate whether the IPFP radiomics score was independently associated with pain progression.</div></div><div><h3>RESULTS</h3><div>The baseline IPFP radiomics model showed stable predictive performance, with AUCs of 0.731 in internal validation and 0.732 in the external test set. This exceeded the performance of clinical-feature models (AUC 0.534-0.587) and MOAKS-based models (AUC 0.532-0.586). The two-year delta-radiomics model further improved prediction, achieving an AUC of 0.861. In multivariable regression, the IPFP radiomics score remained an independent predictor of KOA pain progression.</div></div><div><h3>CONCLUSION</h3><div>Quantitative MRI radiomics of the IPFP may provide a robust imaging biomarker for predicting KOA pain progression. Incorporating IPFP radiomics, particularly longitudinal delta-radiomics, may improve risk stratification beyond clinical features and conventional semi-quantitative MRI assessment.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100452"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A NOVEL APPROACH USING UPRIGHT OPEN MRI TO MEASURE MENISCAL EXTRUSION IN WEIGHT BEARING – FEASIBILTY AND RELIABILITY ASSESSMENT","authors":"J.S. Broberg,&nbsp;H. Zhang,&nbsp;P. Lodhia,&nbsp;D.R. Wilson","doi":"10.1016/j.ostima.2026.100423","DOIUrl":"10.1016/j.ostima.2026.100423","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Meniscal extrusion, defined as abnormal projection of the meniscus beyond the edge of the tibial plateau, has been strongly linked to the progression and severity of knee OA. This link is likely due to the inability of the extruded meniscus to properly distribute load, resulting in greater stress on the cartilage. Meniscal extrusion is often the result of a meniscal tear, and can be corrected as part of surgical repair, however the effectiveness of current surgical approaches is not known. While extrusion can be detected radiologically with MRI before and after surgery, current assessment with clinical MRI scanners requires the knee to be in an unloaded and extended position. Since menisci distribute loads across a range of knee motion, measuring extrusion in weightbearing and flexed positions would give better insight into the mechanical changes caused by injury and, crucially, whether current repair methods better restore knee mechanics to normal. Upright open MRI allows for knee imaging in weightbearing and flexed positions, but the tradeoff is worsened image resolution due to lower field strengths. Therefore, it is important to establish the reliability of meniscal extrusion measurements in weightbearing and flexed postures using upright open MRI.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVES&lt;/h3&gt;&lt;div&gt;1) Develop an upright open MRI protocol for studying meniscal extrusion in weightbearing and flexed positions, and 2) Assess the reliability of measuring meniscal extrusion.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;We recruited four healthy participants (3 female/1 male, ages 24-28) and scanned their right and left knees individually in four different postures with a 0.5T upright open MRI scanner (Figure 1). Each participant had two identical scanning sessions for reliability analysis. Dixon sequences were used to obtain coronal images of the knee (TR = 430.7 s, TE = 7 s, FOV = 17 × 17 × 6 cm, slice thickness = 3.5 mm, slice gap = 0.5 mm, voxel dimensions = 0.56 × 0.41 × 3.5 mm, flip angle = 40°, bandwidth = 158.8 Hz/pixel, total scan time = 3 min 10 s). The postures were: a) supine with knees extended; b) unloaded seated at 90° of knee flexion; c) weightbearing standing with knees extended; and d) weightbearing assisted squat with knees flexed to 90°. An MRI-compatible sloped seat supported participants in assisted squatting while still allowing for joint loading and muscle activation. Medial and lateral meniscal extrusions were calculated on a midcoronal slice as the distance between the edge of the tibial plateau and the external edge of the meniscus. Test-retest reliability was reported using intra-class correlation coefficients (ICC), standard errors of measurement (SEM), and smallest detectable changes with 95% confidence (SDC&lt;sub&gt;95&lt;/sub&gt;).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Mean medial extrusion was 2.5 mm for supine, 1.9 mm for sitting, 2.9 mm for standing, and 2.1 mm for squatting. Mean lateral extrusion was 0.7 mm for su","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100423"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NOVEL GENETICALLY DRIVEN OA RISK SCORE REQUIRES ONLY PELVIC RADIOGRAPHS: A LONGITUDINAL MULTICOHORT STUDY","authors":"F. Boel,&nbsp;Y. Wu,&nbsp;G. Senevirathne,&nbsp;M.A. van den Berg,&nbsp;Z. Xiong,&nbsp;M.M.A. van Buuren,&nbsp;H. Ahedi,&nbsp;L. Arbeeva,&nbsp;N. Arden,&nbsp;S.M.A. Bierma-Zeinstra,&nbsp;F.M. Cicuttini,&nbsp;T.F. Cootes,&nbsp;J. Kemp,&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;R. Agricola","doi":"10.1016/j.ostima.2026.100417","DOIUrl":"10.1016/j.ostima.2026.100417","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Hip OA is highly heritable, with genetics accounting for up to 60% of hip OA risk, possibly partly mediated by hip morphology. A proxy for genetically-driven morphology could enhance OA risk prediction when an individual’s genetic data is unavailable. Previously, the World COACH consortium identified 10 genome-wide significant loci associated with OA-free hip shape using Combined GWAS (C-GWAS). OA-free hip shape was described using statistical shape modeling (SSM), yielding shape modes that quantify individual shape variance within a population. Several of these loci were previously shown to be associated with hip OA using GWAS. These overlapping loci provide a basis for using hip morphology to construct a phenotypic proxy for OA risk.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;We hypothesized that a proxy score capturing genetically driven hip morphology patterns on pelvic radiographs can predict OA risk. This study aimed to 1) construct a novel OA risk score, and 2) investigate whether this score is associated with radiographic hip OA (RHOA) development 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 12,672 individuals (21,735 hips) within the World COACH consortium with available biological sex, baseline age, and baseline and 4-8 year follow-up RHOA grades. Of these participants 70.5% were female with a mean baseline age of 62.4 ± 8.4 years. RHOA was quantified using Croft, KLG, or modified grades, and harmonized as “No OA” (any score 0), “doubtful OA” (any score 1), or “definite OA” (any score ≥ 2 or THR). Hip morphology was quantified using an SSM of 80 automatically placed landmarks outlining the proximal femur and hemipelvis on the baseline anteroposterior pelvic radiographs. The OA risk score was constructed by merging the SSM shape modes associated with OA-related loci, weighted by their respective C-GWAS effect sizes. This yielded a score per hip that quantifies it’s similarity to the genetically defined OA-related shape. Figure 1 visualizes the different steps of creating the OA risk score. A one-stage logistic regression model with generalized mixed effects accounting for hip side, individual, and cohort was used to assess the association between the novel OA risk score and RHOA development within 4-8 years. The score was modelled absolute to reflect increased risk of RHOA development at both extremes with a unit size of 0.01 SD. Models were adjusted for baseline age and biological sex. Statistical significance was set at p&lt;0.05.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Within 4-8 year follow-up, 2.2% of hips developed definite RHOA. The OA risk score was constructed by merging 9 different shape modes. Each 0.01 SD increase in the absolute OA risk score was associated with 1% higher odds of RHOA development (adjusted OR 1.01, 95% CI 1.006-1.017, p &lt; 0.001).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;This novel score effectively quantifies a genetically defined","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100417"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Welcome to the 20th IWOAI","authors":"","doi":"10.1016/S2772-6541(26)00068-1","DOIUrl":"10.1016/S2772-6541(26)00068-1","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100458"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IMAGING OF THE BONE ARCHITECTURE IN THE CONTEXT OF ALZHEIMER DISEASE","authors":"V Suryadevara,&nbsp;A Kambrath Valiya,&nbsp;CJ Krehbial,&nbsp;S Karra,&nbsp;M Kluppel,&nbsp;MS Willis","doi":"10.1016/j.ostima.2026.100445","DOIUrl":"10.1016/j.ostima.2026.100445","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Osteoarthritis (OA) and related symptom burden is associated with an increased risk for developing Alzheimer’s Disease (AD). There is increasing evidence showing musculoskeletal changes occurring during AD. Falls and fractures are common in AD, making bone health management crucial. Early-stage AD has lower bone mineral density, which correlates with brain volume and cognitive decline. This warrants investigating the impact of changes in the bone beyond that tissue.</div></div><div><h3>OBJECTIVE</h3><div>To identify structural and functional changes in the bone to visualize OA phenotyping changes in the preclinical models of AD.</div></div><div><h3>METHODS</h3><div>To characterize the bone microarchitecture i.e. cortical and trabecular microarchitecture, Skyscan 1172 System, Bruker, Kontich, Belgium, V=60 kV, I=167 μA with rotation steps of 0.4° was used for µCT to scan the femur of 12-month-old PSEN1 KI/hAPP Tg+, FDD Tg+, MAPT P301S Tg+ and C57BL/6J mice, N = 20/group (equal number of male and female). We reconstructed the μCT scans using NRecon (Version 1.4.7.2, 2005-11, Microphotonics, Allentown, PA) calibrated to hydroxyapatite-mimicking phantoms (0.25 and 0.75 g/cm³ Ca-HA). We utilized NRecon (Version 1.4.7.2, 2005-11), Dataviewer (1.5.4.6, 2004-11, 2012-17), CTAn (1.18.4.0 +, 2003-11,200) to reconstruct, visualize, inspect, and analyze the scans, according to the manufacturer’s protocols. We assessed trabecular properties (distal femur metaphysis region, beginning proximally 0.5 mm to the growth plate, extending proximally 1 mm) and cortical parameters (at the mid-diaphysis region) using a custom MATLAB code. Three-point bending tests were conducted using a servo-hydraulic test system (Test Resources Inc., Shakopee, MN, USA) to evaluate the biomechanical qualities.</div></div><div><h3>RESULTS</h3><div>Female Psen1 mutation(L166P) mice were found to have reductions in whole-body mineral density (BMD) and bone mineral content as early as one month and a sustained bone phenotype with age with loss in cortical and trabecular properties compared to age-and gendermatched wildtype control mice, whereas male mice did not (Fig.1A-C). In Tau P301S (PS19) Tg+ mice, the cortical and trabecular properties were altered mostly in males, whereas the biomechanical properties were altered both in male and female mice (Fig.1E). FDD Tg+ mice, which show amyloid, tau, and neuroinflammation, had alterations in cortical, trabecular and mechanical properties in both males and females at 12 months and increased bone resorpion (Fig.1F). Generally, among all murine cohorts, PSEN, APP (Fig. 1D) and Tau protein expression is observed in the bone, a potential crosstalk between the brain and the bone in AD.</div></div><div><h3>CONCLUSION</h3><div>Our imaging studies in preclinical models of AD suggest sex-specific changes in the bone during onset of amyloid pathology (Table1), indicating OA phenotype during AD.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100445"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI RADIOMICS REFLECT MICROSTRUCTURAL PATTERNS ASSOCIATED WITH ACETABULAR COVERAGE IN YOUNG ADULTS","authors":"M.A. Kamphuis,&nbsp;E.H.G. Oei,&nbsp;J. Runhaar,&nbsp;S.M.A. Bierma-Zeinstra,&nbsp;S. Klein,&nbsp;J. Hirvasniemi","doi":"10.1016/j.ostima.2026.100406","DOIUrl":"10.1016/j.ostima.2026.100406","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Abnormal hip morphology, bone microstructure, and cartilage composition have independently been linked to OA development at an early age. However, it remains unclear whether hip morphology is associated with underlying bone and cartilage microstructural properties. Understanding this relationship could reveal early mechanistic pathways in OA and support imaging-based risk assessment.</div></div><div><h3>OBJECTIVE</h3><div>The aim of this study was to determine whether MRI derived radiomic features of femoral bone, acetabular bone, and cartilage were associated with different measures of acetabular coverage and to identify which radiomic feature contributed most to this association.</div></div><div><h3>METHODS</h3><div>We analyzed data from 2448 participants from the Generation R Cohort (mean ± standard deviation age and BMI: 18.4±0.6 years and 22.7±3.8 kg/m²) comprising 1196 males and 1252 females. For the analysis, a T1-weighted Dixon (Lava Flex) sequence was acquired on a 3T system, with an in-plane resolution of 0.89 × 0.89 mm² and a slice thickness of 1.2 mm. Femoral and acetabular bone, and cartilage were automatically segmented using a previously trained nn-UNet. 481 radiomic features were extracted per structure, from the in-phase outputs, using the open-source Workflow for Optimal Radiomics Classification (WORC) framework. Six acetabular coverage metrics representing global and regional morphology were computed from the segmentations. Associations between radiomic features and coverage measures were assessed using a linear (ElasticNet) and nonlinear (TabPFN) regression approach, with 10-fold cross validation. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) and performance was quantified using cross validated R² in Python.</div></div><div><h3>RESULTS</h3><div>Across all analyzed structures, posterolateral and anteromedial coverage measures showed the strongest associations with radiomic features (R² = 0.39–0.76) for both the ElasticNet and TabPFN modelling approaches. Texture based features, particularly those derived from the gray level cooccurrence matrix and Gabor filters, consistently demonstrated the highest predictive value.</div></div><div><h3>CONCLUSION</h3><div>Radiomic features derived from T1-weighted MRI partially explain variation in acetabular coverage, suggesting that hip morphology is already linked to distinct microstructural tissue properties in young adults. The findings further highlight that radiomics captures information beyond morphology alone, highlighting its potential as a complementary, non-invasive tool for studying OA and exploring additional tissue-level biomarkers of disease risk and progression.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100406"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}