Osteoarthritis imaging最新文献

{"title":"HIP MORPHOLOGY DEVELOPMENT FROM CHILDHOOD TO YOUNG ADULTHOOD","authors":"N. Hendriks,&nbsp;F. Rivadeneira,&nbsp;C. Lindner,&nbsp;S.M.A. Bierma-Zeinstra,&nbsp;R. Agricola,&nbsp;J. Runhaar,&nbsp;F. Boel","doi":"10.1016/j.ostima.2026.100405","DOIUrl":"10.1016/j.ostima.2026.100405","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Hip morphology is marked as important risk factor for the development of hip OA in relatively young individuals. There may be unknown morphological factors that provide insight into the development of specific hip morphologies and, in the long term, contribute to the prevention of hip OA since limited effective preventative strategies currently exist. A step towards understanding hip morphology development and related risk factors is to create growth models to quantify morphological development.</div></div><div><h3>OBJECTIVE</h3><div>This study aimed to create a growth model for hip morphology from childhood to young adulthood in males and females separately.</div></div><div><h3>METHODS</h3><div>Data from the Generation R Study were used, a population-based cohort of participants examined around ages 6, 9, 13 and 18 years. We selected participants with high-resolution anteroposterior (AP) dual-energy x-ray absorptiometry (DXA) of the right hip at three or four timepoints. In total, 1610 participants were included. Hip shape was described with 36 landmarks outlining the acetabular roof, teardrop, femoral head and neck using the software Bonefinder®. The radiological landmarks could be identified at all timepoints. With the same software, the statistical shape model (SSM) was created to quantify hip morphology in the population. Linear mixed-effects models were used to describe the longitudinal changes in shape related to sex assigned at birth and age. All shape modes explaining at least 2% of the total variation in shape within our population were analyzed.</div></div><div><h3>RESULTS</h3><div>A total of 5141 observations were included, of which 3881 from participants with three timepoints, and 1260 from participants with four timepoints. Six shape modes explained 85% of the total variation and were analyzed. All shape modes showed a relationship with age and linear mixed-effects models improved by incorporating interaction with sex assigned at birth. Shape modes 1 – 3 (SM1, SM2, SM3) were highlighted in Figure 1. The first shape mode shows the largest change (in SDs) over time and could be partially interpreted as calcification of the femoral head over time (Fig. 1: SM1). Shape mode 2 shows the femoral head sphericity, possibly related to the calcification, and relative femoral head size compared to the acetabulum (Fig. 1: SM2). Shape mode 3 shows variation in the coverage and lateral neck junction shape (Fig. 1: SM3).</div></div><div><h3>CONCLUSION</h3><div>This exploratory study provides insight into the morphological development of the hip joint from childhood to young adulthood, describing the development of the entire hip joint with age. The model could be used to identify potential developmental risk factors related to hip morphology, which may be relevant for the early detection and prevention of hip morphologies related to hip OA.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100405"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543639","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":"PREDICTING TIME-TO-PAIN PROGRESSION IN KNEE OSTEOARTHRITIS: A CLINICAL AND RADIOMICS FEATURE ANALYSIS","authors":"N. Machlovi,&nbsp;R. Soyak,&nbsp;E. Montin,&nbsp;K. Cho,&nbsp;V. Mazzoli,&nbsp;M. Lattanzi,&nbsp;C.M. Deniz,&nbsp;O. Cigdem","doi":"10.1016/j.ostima.2026.100434","DOIUrl":"10.1016/j.ostima.2026.100434","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Knee osteoarthritis (KOA) is a prevalent joint disorder causing significant physical disability worldwide. Estimating time-to-pain progression could help identify high-risk patients and guide treatment decisions. While prior studies have explored binary progression prediction, predicting the specific year of pain onset within a multi-year timeframe remains underexplored. Pain is inherently subjective and multifactorial, and it is unclear whether baseline clinical assessments or structural imaging features carry sufficient signal to predict when pain progression will occur.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To investigate whether baseline clinical variables or MRI-derived radiomics features can predict the year of knee pain progression (Years 1–8) in KOA patients.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;We utilized data from the Osteoarthritis Initiative (OAI) database. Subjects with a baseline WOMAC pain score increase of ≥9 (scaled 0–100) sustained for at least two years were classified as having knee pain progression. Only progression cases were included. From 1,224 baseline clinical variables in OAI, Lasso regularization optimized via Optuna selected 18 features. A self-supervised pretrained TabNet classifier was trained for 8-class time-to-progression prediction (Years 1–8) using masked feature reconstruction followed by supervised fine-tuning with cross-entropy loss. For comparison, 3D radiomics features extracted from bones and soft tissue segmentation masks of baseline DESS MRI were combined with Lasso feature selection and XGBoost classification (Figure 1). Data was split into training/validation/test sets (n=2,064/306/589). Performance was evaluated using accuracy within ±1 year and mean absolute error (MAE) in years.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;The TabNet model using clinical variables achieved 57.8% accuracy within ±1 year and MAE of 1.72 years. Feature importance analysis identified KOOS Quality of Life, SF-12 Physical Summary Scale, WOMAC Pain Scores (for affected and contralateral knee), CES-D Depression Score, and knee pain severity as the most attended features. However, the model predominantly predicted early progression years (Year 1 recall=0.83) while failing to identify later progression time points (Years 5–8 recall≈0), indicating that it largely defaulted to predicting the majority class rather than learning meaningful temporal patterns. The radiomics-based approach (Lasso + XGBoost on DESS features) performed worse, achieving 48.2% accuracy within ±1 year. Feature selection yielded 20 radiomic features spanning six anatomical regions (femur, femur cartilage, tibia, tibia cartilage, patella cartilage, and meniscus), including first-order, texture, and shape features extracted using wavelet, logarithm, exponential, LBP-3D, and gradient filters. Neither approach demonstrated sufficient discriminative ability for fine-grained temporal prediction of pain progression.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100434"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543686","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":"MEDIAL FIXED JSW TRAJECTORIES: PREDICTORS OF TRAJECTORIES AND THEIR ASSOCIATION WITH THE RISK OF KNEE REPLACEMENT","authors":"C.K. Kwoh,&nbsp;R. Tang,&nbsp;E.J. Bedrick,&nbsp;J. Crawmer,&nbsp;Z. Li,&nbsp;J. Liew,&nbsp;T. Neogi,&nbsp;X. Sun,&nbsp;J. Duryea","doi":"10.1016/j.ostima.2026.100420","DOIUrl":"10.1016/j.ostima.2026.100420","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Loss of JSW is a hallmark of knee OA structural progression, and knee replacement (KR) is considered by regulatory agencies as a hard clinical endpoint in knee OA DMOAD clinical trials. There has been limited longitudinal investigation of JSW trajectories and their association with the risk of KR, however. In addition, identification of predictors of worse vs. better JSW loss trajectories may help identify potential targets for therapeutic interventions and may also be useful for selecting more homogenous study populations.</div></div><div><h3>OBJECTIVE</h3><div>This study aimed to 1) identify distinct JSW trajectories; 2) identify predictors of different rates of change in medial fixed JSW (fJSW) trajectories; and 3) examine the association between JSW trajectories and the risk of KR.</div></div><div><h3>METHODS</h3><div>Knees (n = 2862) with available baseline MRI Osteoarthritis Knee Scores (MOAKS) from OAI were included. Observations collected after KR were excluded. Medial fJSW at 25% of femur width (i.e., x=0.250) was assessed longitudinally at baseline and each follow-up visit (i.e., 12, 24, 36, 48, 72, 96, and 120 months) to construct change-from-baseline JSW trajectories. Latent Class Mixed Models (LCMM) were employed to classify knees into two clusters having distinct fJSW trajectories, with (fJSW) change from baseline as the outcome and quadratic year term as the predictor, while jointly identifying predictors of latent class membership within the model. Covariates included demographic, clinical, psychosocial and imaging features. The response covariance matrix was allowed to be different for each latent class. Kaplan-Meier curves were used to illustrate time to KR for each fJSW trajectory cluster, and differences between clusters were assessed using the log-rank test.</div></div><div><h3>RESULTS</h3><div>Two fJSW trajectory clusters were identified: a faster progressive trajectory (n = 824) and a slower progressive trajectory (n = 2038) (Figure 1). Predictors of membership in the faster progressive trajectory included meniscal extrusion, effusion-synovitis/Hoffa synovitis, cartilage surface/depth damage, larger bone marrow lesion (BML) size, and being overweight/having obesity (i.e., BMI &gt; 25) (Figure 2). The faster progressive trajectory was also associated with a higher risk of KR (p &lt; 2e-16)</div></div><div><h3>CONCLUSION</h3><div>We have identified two distinct JSW trajectories that were associated with MRI-detected structural damage and specific demographic and clinical features. The faster progressive trajectory was associated with an increased risk of KR. Risk factors for the faster trajectory may represent suitable targets for interventions aimed at preventing KR.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100420"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543724","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 THIGH MUSCLE-INFORMED MULTI-TASK DEEP LEARNING SYSTEM FOR PREDICTING KNEE OSTEOARTHRITIS STRUCTURAL AND SYMPTOMATIC PROGRESSION","authors":"Q. Dang,&nbsp;H. Wang,&nbsp;Z. Wang,&nbsp;J. Ma,&nbsp;C. Ding","doi":"10.1016/j.ostima.2026.100414","DOIUrl":"10.1016/j.ostima.2026.100414","url":null,"abstract":"<div><h3>BACKGROUND</h3><div>Knee osteoarthritis (OA) is a prevalent chronic joint disease whose progression imposes a substantial healthcare burden. Accurate individualized prediction is crucial for implementing targeted interventions. Thigh muscle quality is a key contributor to knee OA progression and can be improved through various interventions. Here, we developed MKOTPR (muscle-oriented knee osteoarthritis tailored progression risk), a multi-task deep-learning-based system that enables end-to-end analysis of raw thigh MRI and outputs probabilities of structural and symptomatic progression of knee OA.</div></div><div><h3>METHODS</h3><div>A total of 4023 thigh MRI scans were collected from five clinical centers, forming training, validation, internal testing, and two external testing sets. Model performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Time-dependent receiver operating characteristic (ROC) analyses were conducted to evaluate performance at specific follow-up time points, and Kaplan-Meier curves were generated to illustrate outcomes between risk groups. To enhance interpretability, MKOTPR was correlated with established clinical risk factors. Its independent predictive value was evaluated in the clinical context. Grad-CAM was used to elucidate decision-relevant information captured by MKOTPR.</div></div><div><h3>RESULTS</h3><div>For predicting structural progression, MKOTPR yielded AUCs of 0.935, 0.908, and 0.935 in the internal and two external testing sets, respectively, and effectively stratified patients into low- and high-risk groups. For predicting symptomatic progression, MKOTPR achieved AUCs of 0.957, 0.945, and 0.960 across testing sets, with similarly effective risk stratification. The MKOTPR also exhibited favorable accuracy, sensitivity, specificity, PPV, NPV, and F1 scores for both outcomes. Time-dependent ROC analyses further demonstrated its good discriminative performance at years 1–4. The observed alignment between model predictions and known clinical factors confirmed MKOTPR’s clinical relevance. MKOTPR emerged as an independent prognostic factor for knee OA progression. The Grad-CAM visualized important regions that informed MKOTPR in making predictions. Moreover, model performance remained robust in subgroup analyses.</div></div><div><h3>CONCLUSION</h3><div>By performing entire-thigh muscle analysis on MRI, MKOTPR simultaneously predicted structural and symptomatic progression of knee OA. Such a risk stratification tool may help guide muscle-targeted interventions to optimize patient outcomes.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100414"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543815","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":"OPTIMIZATION OF PHOTON-COUNTING CT FOR BONE IMAGING USING ULTRA-HIGH-RESOLUTION MODE","authors":"TS Patzer,&nbsp;R Kumar Panta,&nbsp;FR Schwartz,&nbsp;C Dunning,&nbsp;AJ Huang,&nbsp;R Balza,&nbsp;OM Fathy,&nbsp;ML Bouxsein,&nbsp;F Johannesdottir,&nbsp;M Jarraya","doi":"10.1016/j.ostima.2026.100439","DOIUrl":"10.1016/j.ostima.2026.100439","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;BACKGROUND&lt;/h3&gt;&lt;div&gt;While ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) shows promising agreement with high resolution peripheral quantitative CT for bone microarchitecture evaluation, clinical translation requires balancing noise, resolution, and dose. Conventional optimization relies on objective metrics and may not reflect task-based perception. A hybrid approach integrating objective and subjective assessment may better guide the development of clinically robust PCD-CT UHR bone imaging protocols.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;PURPOSE&lt;/h3&gt;&lt;div&gt;To apply a hybrid objective–subjective approach to optimize PCD-CT UHR bone imaging protocol at clinically acceptable dose levels.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Four cadaveric specimens (one lumbar vertebra and three proximal femora) along with the Mindways phantom (Model 3), were imaged with PCD-CT (NAEOTOM Alpha.Peak VB20, Siemens Healthineers) at 120kVp. Six total acquisitions were performed at three dose levels, IQ-75 (3.0–4.3mGy), IQ-150 (6.0–8.6mGy), and IQ-450 (17.9–25.7mGy) across two pitch settings (0.35 and 0.85). For each acquisition, images were reconstructed using three kernels (Br76, Br89, Br98) and two levels of Quantum Iterative Reconstruction (QIR2 and QIR4), yielding 35 unique protocols. Protocol performance was evaluated using both objective and subjective assessment. Objective metrics included SNR, CNR, and task-based transfer function (TTF) f50 for spatial resolution. Task-based protocol performance was independently assessed by four radiologists using a 5-point Likert scale for cortical sharpness, trabecular visibility, and noise. Statistical analysis for SNR, CNR, TTF-f50 and Likert ratings were performed, and heatmaps were generated to evaluate protocol performance. Interrater reliability was assessed using a two-way random-effects, absolute-agreement intraclass correlation coefficient (ICC).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;SNR and CNR were highest with Br76 kernel, QIR4, and highest exposure (IQ-450), while noise was lowest for Br76, increased with sharper kernels (Br89, Br98) and lower IQ, and was markedly reduced with QIR4; pitch had minimal impact (figures 1, 2). Variance analysis showed that QIR had the greatest impact (η² = 0.54, 0.52), followed by kernel (η² = 0.29, 0.32) and IQ (η² = 0.09, 0.08), while pitch had no significant effect. For Br76, TTF-f50 was consistently higher with QIR2 than QIR4 (1.62 vs 1.36 at IQ-75, pitch 0.85), with minimal dependence on IQ and pitch. Consistently, Br76 achieved the highest scores (mean Likert 4.2), outperforming Br89 (3.88) and Br98 (3.02), with scores improving with increasing IQ and at QIR2, and minimal impact from pitch. Interrater reliability was excellent (ICC = 0.92).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;Br76 provided the best objective-subjective performance for trabecular bone assessment, with QIR4 minimizing noise and maximizing SNR/CNR, while QIR2 better preserved spatial resolution; pitch ha","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100439"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543635","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":"MOLECULAR MRI OF CARTILAGE SENESCENCE IN OSTEOARTHRITIS USING A BETA GAL BIORESPONSIVE CONTRAST AGENT","authors":"H.E. Daldrup-Link,&nbsp;Y.U. Lokesha,&nbsp;Y. Tanyildizi,&nbsp;S. Jayakumar,&nbsp;S. Goodman,&nbsp;T.J. Meade","doi":"10.1016/j.ostima.2026.100415","DOIUrl":"10.1016/j.ostima.2026.100415","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Cellular senescence in osteoarthritis (OA) cartilage is characterized by increased beta galactosidase (Gal) activity. We developed a beta Gal bio responsive contrast agent (BCA) with a caged Gd&lt;sup&gt;III&lt;/sup&gt; center. Beta Gal cleavage unmasks the central Gd&lt;sup&gt;III&lt;/sup&gt; coordination site, increasing proton interaction and resulting in increased T1 signal.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This study tested whether beta Gal BCA can detect senescent cells in human OA cartilage. We hypothesized that (i) beta Gal BCA produces greater cartilage T1 enhancement than gadobutrol as control, and (ii) that cartilage T1-enhancement on beta Gal BCA enhanced MRI correlates with histologic senescence markers.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;This IRB approved prospective study included resected knee or hip joint specimens from 20 patients with OA (n = 20, age 69 ± 11 years, 15 women, 5 men). To simulate an MR arthrography, fresh specimens were incubated with 10 ml of either beta Gal BCA (n = 10) or gadobutrol (n = 10) at 2.5 mM for 30 minutes. Pre and post contrast MRI included proton density and T2 weighted fast spin echo, T1 weighted fast spin echo, and saturation recovery T1 mapping sequences. Pre versus postcontrast cartilage T1 relaxation times within each group as well as postcontrast T1 relaxation times between contrast agent groups were compared with Wilcoxon rank sum testing. Associations of post contrast T1 with T2, Outerbridge score, and immunohistochemistry senescence markers (p16, p21, and beta Gal) were assessed using linear regression and linear mixed effects modeling. Medians and interquartile ranges are reported.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;For beta Gal BCA, cartilage T1 was significantly shorter post contrast (227 [IQR 51] ms) than pre contrast (1469 [IQR 172] ms, P = 0.002). Post contrast T1 after beta Gal BCA was strongly inversely correlated with cartilage T2 (Rc2 = Rm2 = 0.77, P &lt; 0.001).&lt;/div&gt;&lt;div&gt;For gadobutrol, cartilage T1 was also significantly shorter post contrast (993 [IQR 440] ms) than pre contrast (1475 [IQR 88] ms, P = 0.002), but post contrast T1 showed no correlation with T2 (Rc2 = 0.34, Rm2 = 0.00, P = 0.818). Cartilage enhancement on T1 maps was higher with beta Gal BCA (delta T1 85% [IQR 5%]) than gadobutrol (delta T1 34% [IQR 32%], P &lt; 0.001). Post contrast cartilage T1 on beta Gal BCA enhanced MRI showed strong negative correlations with p16 (Rc2 = Rm2 = 0.95, P &lt; 0.001), p21 (Rc2 = 0.94, Rm2 = 0.92, P &lt; 0.001), and beta Gal staining (Rc2 = Rm2 = 0.92, P &lt; 0.001). No significant correlations were observed for gadobutrol with p16 (Rc2 = 0.26, Rm2 = 0.01, P = 0.661), p21 (Rc2 = 0.32, Rm2 = 0.01, P = 0.597), or beta Gal (Rc2 = 0.40, Rm2 = 0.03, P = 0.482). For all marker associations, slopes differed significantly between the two contrast agents (P &lt; 0.001).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;Beta Gal BCA provides enzyme responsive cartilage T1 contrast ","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100415"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543816","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":"STATISTICAL SHAPE MODELING OF THE HIP JOINT SPACE CAPTURES RADIOGRAPHIC SEVERITY AND PROGRESSION PATTERNS","authors":"F. Boel,&nbsp;G. van Tulder,&nbsp;M.A. van den Berg,&nbsp;M.M.A. van Buuren,&nbsp;C. Lindner,&nbsp;J. Runhaar,&nbsp;S.M.A. Bierma-Zeinstra,&nbsp;R. Agricola","doi":"10.1016/j.ostima.2026.100450","DOIUrl":"10.1016/j.ostima.2026.100450","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;The definition of radiographic hip OA (RHOA) is still dependent on semi-quantitative grading systems such as the KLG and the (modified) Croft. These semi‑quantitative systems rely heavily on reader expertise, limiting reproducibility and comparability across studies. JSW is currently the most reliable radiographic feature for describing hip OA, and continuous JSW measurements offer a promising route toward automated, objective, and reproducible RHOA assessment. Joint space shape and change over time could provide an opportunity to quantify structural variation and progression in a continuous manner without reducing the joint space to a single measure. However, it remains unclear whether specific joint space shape can capture longitudinal change, OA severity, or both.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To investigate whether joint space shape derived from longitudinal anteroposterior (AP) pelvic radiographs can capture 1) time‑dependent changes, 2) RHOA severity, or 3) severity‑dependent progression patterns.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;We included 955 participants (6754 observations) from the prospective Cohort Hip and Cohort Knee (CHECK) study, a longitudinal cohort with follow-up at 2, 5, 8 and 10 years, in our exploratory study. The mean baseline age of the participants was 55 ± 5.2 years and 79% was female. The joint was automatically segmented from the pelvic radiographs using a pre-trained U-Net architecture, which was fine-tuned for hip segmentation. From the segmentation mask, the joint space was described using 100 landmarks, see Figure 1a. These landmarks were used to create a statistical shape model (SSM) to describe joint space shape variation in our sample. Shape modes explaining ≥ 2% of total variation were selected for analysis. RHOA was quantified using KLG. The KLG was categorized into an ordinal 3-level scale: “free of OA” (KLG = 0), “doubtful OA” (KLG = 1), and “definite OA” (KLG ≥ 2). Recategorization of the KLG was done to create a balanced distribution, with 38.5% of observations free of OA, 35.2% doubtful OA, and 26.3% definite OA. Hips with total hip replacements where excluded. Linear mixed-effects models were fitted for each shape mode to investigate 1) time dependency of the shape mode, 2) relationship with RHOA severity, 3) severity‑dependent progression patterns modeled with and without an interaction between time and RHOA. Mixed effects were included to account for hip side and individual. Model comparisons were performed using likelihood ratio tests. Polynomial contrasts (linear/quadratic) were used to model ordinal RHOA.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;The first two shape modes explained 98% of total shape variance in the population. A visualization of the shape modes 1 and 2 is shown in Figure 1b and 1c, respectively. Shape mode 1 changed significantly over time (p &lt; 0.001), as well as with RHOA severity (linear: p &lt; 0.001). The association with severity disap","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100450"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543818","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":"Healthy knee asymmetry is a potential risk factor for knee osteoarthritis: Data from the osteoarthritis initiative","authors":"Mikael J. Turunen ,&nbsp;Alexander Paz ,&nbsp;Lauri Stenroth ,&nbsp;Santtu Mikkonen ,&nbsp;Mimmi K. Liukkonen ,&nbsp;Mika E. Mononen","doi":"10.1016/j.ostima.2025.100385","DOIUrl":"10.1016/j.ostima.2025.100385","url":null,"abstract":"<div><h3>Objective</h3><div>Commonly used grading systems in knee osteoarthritis (OA) evaluation provide an overview of the disease severity with limited prognostic ability. Recently, efforts towards automated and objective deep- and machine-learning, and computational modeling-based prediction tools have been made, but they are complex and lack interpretability. This study aimed to identify knee joint morphology measures that can be easily quantified from plain radiographs and are indicative of the risk of radiographic OA development among subjects without definite radiographic OA, focusing especially on the asymmetry of the knees.</div></div><div><h3>Materials and Methods</h3><div>Knee joint dimensions and angles were measured from anterior-posterior weight-bearing knee radiographs at baseline and 8-year follow-up time point. The subjects were grouped based on Kellgren-Lawrence grades at the 8-year follow-up and compared with regard to the knee joint dimensions and angles and their asymmetries between the subjects’ knees.</div></div><div><h3>Results</h3><div>Absolute dimensions or angles at baseline were not associated with OA development. Instead, the asymmetry in the dimensions (relative difference between the left and right knee), was higher in subjects who developed radiographic knee OA during 8-year follow-up. The medial joint space asymmetry was associated with the development of advanced knee OA when it was over 10 % (OR = 1.87) or 15 % (OR = 3.27).</div></div><div><h3>Conclusions</h3><div>Medial joint space asymmetry between the left and right knee of over 10 % could be a potential risk factor for developing knee OA.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 4","pages":"Article 100385"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145712169","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":"Analytic challenges in defining structural phenotypes in OA clinical trials: a perspective","authors":"Samantha Chin ,&nbsp;Jamie E. Collins","doi":"10.1016/j.ostima.2025.100273","DOIUrl":"10.1016/j.ostima.2025.100273","url":null,"abstract":"<div><div>Knee osteoarthritis (OA) is a disease that can cause substantial pain and disability in patients. The progression of OA has been linked to inflammatory, mechanical, genetic, and metabolic factors, yet patterns of symptoms and structural damage vary considerably between knee OA patients. The heterogeneity of the disease presents a need for identifying and understanding patient subgroups to inform more personalized treatments. Identifying anatomical morphotypes, a type of classification defined by anatomical and morphological attributes, is critical for identifying subgroups of patients who are most likely to benefit from particular treatments. Cluster analysis is an unsupervised learning method that can be used to uncover subgroups in datasets without labeled outcomes to guide the analysis. In this perspective, we will review analytic challenges in identifying anatomical morphotypes using clustering methods, including finding patterns that are not clinically relevant, navigating the unique correlation structure of anatomical data, and working with high dimensional data. With the exciting applications of clustering methods to improve personalized medicine in knee OA, it is essential to consider these analytic challenges to ensure that analyses yield clinically actionable insights.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 4","pages":"Article 100273"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145712170","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":"Erratum regarding missing Declaration of interests and Ethical approval statements in the previously published articles","authors":"","doi":"10.1016/j.ostima.2025.100375","DOIUrl":"10.1016/j.ostima.2025.100375","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"5 4","pages":"Article 100375"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145712500","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}