Osteoarthritis imaging最新文献

{"title":"AN IMAGING STUDY ASSESSING FEASIBILITY AND RELIABILITY OF WHOLE-BODY 68Ga-FAPI PET-CT FOR STRUCTURAL OSTEOARTHRITS EVALUATION","authors":"AK Roemer,&nbsp;R Heiss,&nbsp;C Schmidkonz,&nbsp;JC Collins,&nbsp;MG Raimondo,&nbsp;M Beck,&nbsp;A Guermazi,&nbsp;T Bäuerle,&nbsp;G Schett,&nbsp;M Uder,&nbsp;T Kuwert,&nbsp;A Ramming,&nbsp;FW Roemer,&nbsp;AM Ramming,&nbsp;A Atzinger","doi":"10.1016/j.ostima.2026.100443","DOIUrl":"10.1016/j.ostima.2026.100443","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>The relevance of fibroblastic activation has been acknowledged for several rheumatologic diseases. In rheumatoid arthritis fibroblast-like synoviocyte cells show significant fibroblast activation protein inhibitor (FAPI) tracer uptake in a lesion-specific manner, which correlates with clinical disease activity. While osteoarthritis (OA) is considered a primary non-inflammatory disorder, phases of marked synovial activation are commonly seen during the disease course, which drives clinical and structural progression.</div></div><div><h3>OBJECTIVE</h3><div>To evaluate whether presence of OA as assessed by ordinal grading on whole body computed tomography (CT) is associated with ⁶⁸Ga fibroblast activation protein inhibitor (FAPI) positron emission tomography (PET) tracer uptake as a measure of fibroblastic activation. Secondary aim was to evaluate reliability.</div></div><div><h3>METHODS</h3><div>In a retrospective study design patients who had undergone ⁶⁸Ga-FAPI PET-CT for a spectrum of clinical reasons were included. Whole body CT was assessed for OA using the OsteoArthritis Computed Tomography‐Score (OACT) in multiple joints and the spine. Maximum standard uptake value (SUV_max) was determined correspondingly. Logistic regression and correlation analyses were used to describe associations between structural OA and ⁶⁸Ga FAPI PET activity.</div></div><div><h3>RESULTS</h3><div>54 patients were included. Presence of OA in the acromio-clavicular joints (ACJ) was strongly associated with odds of SUV_max being in the highest tertile. Increased odds were seen for one location of the cervical (OR 5.7, 95% confidence interval [1.6, 20.4]) and both locations of the lumbar spine (OR 8.3, [1.5, 46.6] and 5.0 [1.3, 18.7]). Moderate correlations were observed for OA severity and ⁶⁸Ga-FAPI PET uptake for most joints and spine. Reliability for OA scoring (mean w-kappa) was 0.62 (intra-reader) and 0.67 (inter-reader). Mean intraclass correlation coefficients for SUV_max were 0.76 and 0.82, respectively (Table 1).</div></div><div><h3>CONCLUSION</h3><div>Presence of OA was associated with increased SUV_max uptake for the ACJ and the cervical and lumbar spine. In this feasibility study CT-based OA scoring and ⁶⁸Ga-FAPI PET SUV_max determinations could be achieved with good reliability. Future longitudinal work will have to elucidate whether FAPI uptake actually precedes morphologic joint pathology and whether FAPI-positive and FAPI-negative (or only mildly positive) joints exhibit different trajectories of disease progression, which may have implications for future fibroblast-targeting therapeutic approaches.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100443"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148557878","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":"RATES OF QUANTITATIVELY-DEFINED CARTILAGE LOSS DIFFER BETWEEN DIFFERENT STRUCTURAL KNEE OSTEOARTHRITIS PHENOTYPES - DATA FROM THE FNIH BIOMARKER CONSORTIUM STUDY","authors":"FW Roemer,&nbsp;A Guermazi,&nbsp;JE Collins,&nbsp;DJ Hunter,&nbsp;W Wirth","doi":"10.1016/j.ostima.2026.100444","DOIUrl":"10.1016/j.ostima.2026.100444","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;MRI-based structural stratification may help enrich clinical trial cohorts, considering the expected mode of action of a potential DMOAD candidate. The Rapid OsteoArthritis MRI Eligibility Score (ROAMES) system enables structural phenotyping and may therefore be advantageous to include in clinical trial screening efforts &lt;sup&gt;1&lt;/sup&gt;. Currently, it is unknown whether different structural phenotypes, as defined by ROAMES, exhibit different rates of quantitatively defined cartilage loss over 24 months.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To assess whether knees with a distinct structural phenotype - using two different definitions - exhibit greater rates of femorotibial cartilage thickness loss compared to those with other phenotypes, and to those not exhibiting any particular phenotype. A secondary aim was to evaluate whether the number of concomitant phenotypes increases the risk of cartilage loss.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;The FNIH study was designed as a case-control study within the OAI cohort, with a predetermined number of index knees selected based on clinical and structural outcomes. MRI was performed on 3T systems. MRIs were read according to the MOAKS system. Knees were stratified into either no phenotype, cartilage-meniscus, inflammatory and subchondral bone phenotypes. In addition, we differentiated whether a knee had 0, 1, 2, or 3 phenotypes. We used two definitions for phenotypic stratification, the original ROAMES definition and a published secondary, less stringent definition &lt;sup&gt;2&lt;/sup&gt;.&lt;/div&gt;&lt;div&gt;Quantitative cartilage thickness analysis was based on manual segmentation of the femorotibial cartilage, with blinding to time point. The current study focused on the mean cartilage thickness in the total femorotibial joint. Quantitative cartilage thickness change was computed as an absolute difference between 24 months and baseline value (mm).&lt;/div&gt;&lt;div&gt;Between-group comparisons were performed using analysis of covariance (ANCOVA), adjusting for demographic and clinical variables for the different phenotype subgroups (using both definitions) vs. those without any phenotype and vs. those with another phenotype, separately. In addition, subgroups defined by the number of phenotypes present were compared.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;484 Kellgren-Lawrence 2 and 3 knees were included. The mean age of the participants was 62 years, 60% were women; the average BMI was 30 kg/m&lt;sup&gt;2&lt;/sup&gt;. According to the primary definition, 362 (75%) did not fulfil criteria for any phenotype, while 94 (19%) exhibited the bone phenotype, 22 (5%) the cartilage/meniscus phenotype and 19 (4%) the inflammatory phenotype. When applying the less stringent secondary definitions, these numbers were 274 (57%), 94 (19%), 104 (21%), and 102 (21%), respectively. Using the original ROAMES definition, knees with a bone phenotype showed greater rates of cartilage loss compared to those without a phenotype (mean adj. dif","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100444"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148557879","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":"DCE-MRI–DERIVED TISSUE PERFUSION IS DIFFERENTIALLY ASSOCIATED WITH TIBIAL AND FEMORAL BONE DENSITY IN PEOPLE WITH EARLY KNEE OSTEOARTHRITIS","authors":"K. Kim,&nbsp;S. Zhang,&nbsp;C. Lin,&nbsp;T. Yu,&nbsp;A. Naraghi,&nbsp;R. Mohankumar,&nbsp;A.K.O. Wong","doi":"10.1016/j.ostima.2026.100408","DOIUrl":"10.1016/j.ostima.2026.100408","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Bone mineral density (BMD) and structure of the knee have been implicated in the pathogenesis of knee OA, but it remains unclear how it is affected by local periarticular inflammation. Dynamic contrast-enhanced (DCE)-MRI quantifies tissue-level perfusion kinetics that may reflect inflammation in knee OA. However, the quantification of periarticular tissue perfusion remains challenged by the lack of automated tools, limiting the ease of bone-inflammation investigations.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To understand how periarticular perfusion (between, within and just around the tibia and femur) relates to bone structural and densitometric properties in the knee joint in individuals with knee OA.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;This study included females and males 40-85 years old with knee pain that occurred ≥ 3 times a week, each occurrence &gt; 3 hours. DCE-MRI scans were obtained using a 3.0T MRI with a quadrature knee coil following intravenous administration of 0.1 mmol/kg Gd-DTPA at 2 mL/s. Sagittal T1-weighted fast spoiled gradient echo sequences (TR/TE = 8/3 ms, flip angle = 13°, voxel size: 0.8 × 0.8 × 3.0 mm, 0 mm gap) were obtained at 5.1s temporal resolution for a total of 7.5 minutes. The referent arterial input function (AIF, represented by the femoral artery) was identified by selecting three consecutive slices demonstrating the greatest total amount of contrast enhancement as determined across the full timespan. The periarticular region, excluding the AIF, was represented by combining femoral and tibial bone masks generated using a deep learning model (nnUNet), and dilated for connectivity. Contrast enhanced regions within this periarticular mask were identified by selecting top 5% of voxel intensities, verified by visual inspection (Figure 1). Arterial (C&lt;sub&gt;p&lt;/sub&gt;) and tissue (C&lt;sub&gt;t&lt;/sub&gt;) contrast concentrations over time were measured within the AIF and periarticular contrast regions, respectively. These values were fitted to a standard Toft’s model &lt;figure&gt;&lt;img&gt;&lt;/figure&gt; to estimate the rate of contrast perfusion from the plasma to extracellular space (K&lt;sup&gt;trans&lt;/sup&gt;, volume transfer constant, relative units min&lt;sup&gt;-1&lt;/sup&gt;), representing periarticular inflammation. Volumetric(v)BMD and apparent microstructure were obtained from four separate transaxial peripheral(p) QCT (38 kVp, voxel size: 0.2 × 0.2 × 2.3 mm) scans, each aligned against medial and lateral, femoral and tibial compartments, including total(Tt) and trabecular(Tb).vBMD, and trabecular separation (Tb.Sp), all derived from segmentations achieved by an iterative threshold-based analysis with Laplacian filtering. Associations between periarticular K&lt;sup&gt;trans&lt;/sup&gt; and bone parameters were evaluated using multivariable linear regression, adjusting for age, body mass index (BMI), KLG, glucocorticoid, and antiresorptive medication use.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Among 60 participants (mean age: 61.6(SD:8.0)","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100408"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543642","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-DETECTED SUBCHONDRAL INSUFFICIENCY FRACTURE OF THE KNEE INCREASES RISK OF TOTAL KNEE REPLACEMENT","authors":"D Branco,&nbsp;TS Patzer,&nbsp;M Alzaher,&nbsp;W Issa,&nbsp;T Neogi,&nbsp;J Collins,&nbsp;J Husseini,&nbsp;AJ Huang,&nbsp;WE Palmer,&nbsp;CK Kwoh,&nbsp;A Guermazi,&nbsp;M Jarraya","doi":"10.1016/j.ostima.2026.100421","DOIUrl":"10.1016/j.ostima.2026.100421","url":null,"abstract":"<div><h3>PURPOSE</h3><div>To determine whether MRI-confirmed subchondral insufficiency fracture of the knee (SIFK) is an independent predictor of total knee replacement (TKR).</div></div><div><h3>METHODS</h3><div>Using hospital electronic medical records (EMRs), we assembled a cohort of patients with MRI-detected SIFK and identified a comparator group without SIFK, matched 1:1 for age. All MRIs were performed between November 2021 and November 2024 and subsequently reviewed by four musculoskeletal radiologists 1. to confirm the presence of SIFK and 2. to assess meniscal tear and extrusion using the MRI Osteoarthritis Knee Score (MOAKS). Exclusion criteria included secondary osteonecrosis, prior knee surgery, high-energy trauma, and suboptimal imaging quality. In the present analysis, SIFK was treated as the exposure, and subsequent ipsilateral TKR was the outcome. Patients without documented TKR in the EMR were censored at the date of their last available knee imaging study or clinical encounter. TKR-free survival was estimated using a Kaplan–Meier curve and log-rank testing. A multivariable Cox proportional hazards model was adjusted for sex, age, body mass index, meniscal radial/root tear, meniscal extrusion, and ipsilateral radiographic osteoarthritis.</div></div><div><h3>RESULTS</h3><div>We analyzed a total of 694 patients (344 with SIFK, 350 without SIFK). Patients with SIFK and those without were similar in age at baseline (SIFK+ mean age = 65.35 ± 10.06 years, SIFK- = 66.30 ± 9.96 years). The proportion of women was similar among participants with SIFK and without SIFK (227/344 [66.0%] vs 230/350 [65.7%], respectively). BMI was slightly higher among those with SIF (29.73 ± 6.44 kg/m² versus 28.63 ± 5.72 kg/m² among comparators). The overall mean duration of follow-up was 2.00 ± 1.24 years, with a median of 2.01 years; follow-up was slightly shorter among SIFK+ than SIFK- comparators, with mean durations of 1.82 ± 1.27 years and 2.18 ± 1.19 years, respectively. In total, 90 patients progressed to TKR (64 SIFK+ and 26 SIFK-). Among participants who progressed to TKR, the overall mean time to progression was 0.79 ± 0.70 years, with SIFK+ and SIFK- showing similar mean times to progression of 0.76 ± 0.64 years and 0.88 ± 0.84 years, respectively. In unadjusted analysis, SIFK was associated with conversion to TKR (HR 2.88; 95% CI 1.83–4.55; p&lt;0.001). After adjustment for sex, age, BMI, meniscal root tear, meniscal extrusion, and ipsilateral radiographic osteoarthritis, SIFK remained an independent predictor of TKR (HR 2.06; 95% CI 1.19–3.57; p=0.010) (figure 1).</div></div><div><h3>CONCLUSION</h3><div>In this clinical sample, we found that MRI-confirmed SIFK is an independent predictor for subsequent TKR Future mechanistic studies are needed to further elucidate whether the biomechanical consequences of SIFK on knee joint health are reversible and whether SIFK could serve as a target for preventive strategies to delay TKR.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100421"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543725","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":"TIBIAL GROWTH PLATE THICKNESS DYNAMICS IN COLLAGENASE INDUCED RAT KNEE JOINT OSTEOARTHRITIS","authors":"T. Frondelius,&nbsp;S. Kauppinen,&nbsp;D. Fercher,&nbsp;S. Das Gupta,&nbsp;T. Zhang,&nbsp;G. Barreto,&nbsp;M. Zenobi-Wong,&nbsp;M. Finnilä","doi":"10.1016/j.ostima.2026.100426","DOIUrl":"10.1016/j.ostima.2026.100426","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Longitudinal bone growth occurs in growth plate (GP) via endochondral ossification, where hyaline cartilage is converted to bone. While it is known that glucocorticoids may prevent bone growth, role of OA in GP morphology is not known. Here we developed an automatic method for quantifying the GP thickness (GP.Th) in rat tibias, in order to investigate how this behaves in collagenase induced OA model.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To quantify the effect of collagenase induced OA to the GP.Th of rat tibia.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Male Wistar rats (N=50) were split into unoperated (NAIVE, n=10), sham injected (SHAM, n=20) and collagenase-injected (COL, n=20) groups, where OA was induced via collagenase-injection into the left hind-limb knee. SHAM group had their knee capsule punctured without injection. Unoperated contralateral joints (COL&lt;sub&gt;CL&lt;/sub&gt;, SHAM&lt;sub&gt;CL&lt;/sub&gt;) served as internal controls. The joints were harvested 10 and 60 days after the operation and the tibias were imaged with µCT (Skyscan 1272 Brüker MicroCT, 2.8 µm voxel size) after 22h phosphotungstic-acid (PTA) staining. The µCT-volumes were then down sampled to 11.2 µm resolution. The subsequent analyses are described in Figure 1. AC and subchondral bone (SB) were segmented using U-Net based model. The SB masks were used to select 504 µm deep region below the bone for GP analysis, starting at 56 µm above the bottom edge of the SB. Bone was removed from the GP region using thresholding and denoising, followed by 3D sphere fitting for GP.Th measurement. Maximum projections of GP.Th maps were obtained under manually drawn lateral and medial regions of interest (ROI). Mean GP.Th was calculated for each ROI and the effect of operation was measured using linear mixed effect regression (LMER)&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;The effect of injection and time with 95% confidence intervals (95% CI) to the GP.Th are shown in Figure 2. At the 10-day mark, COL joints have increased GP.Th compared to the COL&lt;sub&gt;CL&lt;/sub&gt; joints in both lateral (14.8 µm, 95% CI: 4.63 µm to 24.9 µm) and medial (23.6 µm, 95% CI: 14.2 µm to 33.0 µm) regions. Medial GP.Th of COL animals is also increased (12.0 µm, 95% CI: 3.60 µm to 20.3 µm) compared to the NAIVE joints. At 60-day mark, COL joints have decreased GP.Th compared COL&lt;sub&gt;CL&lt;/sub&gt; joints (-4.10 µm, 95% CI: -7.87 µm to -0.33 µm) and NAIVE joints (-8.36 µm, 95% CI: -14.2 µm to -2.57 µm) on the lateral side. The SHAM animals have significantly lower GP.Th compared to NAIVE joints (-10.1 µm, 95% CI: -18.8 µm to -1.30 µm for SHAM and -11.2 µm, 95% CI: -20.2 µm to -2.15 µm for SHAM&lt;sub&gt;CL&lt;/sub&gt;) at 10 days on the lateral side, but not on the medial side. The SHAM operated joints show no significant differences in GP.Th compared to the SHAM&lt;sub&gt;CL&lt;/sub&gt; joints. GP.Th decreases over time, as expected (-34.9 µm 95% CI: -39.2 µm to -30.4 µm on lateral side and -32.9 µm, 95% CI: -37.3 µm to -28.4 µm on medi","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100426"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543769","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":"3D QUANTITATIVE MEASUREMENTS OF JOINT SPACING BASED ON SEMI-AUTOMATIC AND AUTOMATIC SEGMENTATION, CONDUCTED 36 MONTHS APART, USING COMPUTED TOMOGRAPHY IMAGES","authors":"J.B. Deloges,&nbsp;S. Uk,&nbsp;J. Cohen,&nbsp;M. Bourguignon,&nbsp;J.D. Laredo,&nbsp;K. Engelke,&nbsp;C. Chappard","doi":"10.1016/j.ostima.2026.100419","DOIUrl":"10.1016/j.ostima.2026.100419","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>Computed tomography (CT) is less frequently used than X-rays or MRI in knee osteoarthritis (OA), but remains very useful for studying changes in the subchondral bone. Furthermore, CT offers good spatial and isotropic resolution, good bone-soft tissue contrast, and as such is a good tool for quantitative imaging in OA. Cartilage degeneration results in variations in joint space (JS) width. quantifying JS width in 3D would enhance the clinical value for monitoring disease progression using CT.</div></div><div><h3>OBJECTIVE</h3><div>We propose a simple method for quantifying the 3D JS variations from CT images, using semi-automatic segmentation as ground truth and automatic segmentation based on deep learning.</div></div><div><h3>METHODS</h3><div>A subset of fifty-four subjects (6 men, 48 women; mean age: 62.8 ± 8.4 years) with OA of the medial compartment (grade 2–3 according to Kellgren-Lawrence) originally recruited for a multicenter longitudinal study (Sequoia - Russia, Belgium and France). High-resolution CT Siemens scans (0.25 × 0.25 × 0.30 mm) in the supine position with knees extended were performed at two timepoints 36 months apart (M00 and M36). Semi-automatic segmentation using ItkSNAP (version 3.8.0), which allowed for manual correction was performed by an expert. Based on the expert’s segmentation, a deep learning model, nnUNet, was trained with Russian data (n=65) and validated based on Belgium and France data (n= 37). The mapping of JS thickness was obtained using the 3D sphere method, applied to the medial (MED) and lateral (LAT) compartments at M00 and M36. Measured parameters were JS volume (JS_vol), mean thickness (JS_mTh), standard deviation of thickness (JS-SDTh), and minimum (JS_min) and maximum (JS_max) values.</div></div><div><h3>RESULTS</h3><div>Individual results of JS_mTh derived from the expert are presented in Figure 1. Comparing the semi-automatic JS_mTh measurements from expert and those obtained by the nn-UNet, the mean DICE coefficients and the Hausdorff distances were 0.86, and 5.1 mm, and 0.83, 7.3 mm for the MED and LAT compartments, respectively. The mean biases are presented in Figure 2 and were -0.15 mm ± 0.38 for the MED and -0.04 mm ± 0.59 for the LAT compartments.</div></div><div><h3>CONCLUSION</h3><div>This study demonstrates that measuring JS_mTh from CT scans in patients with OA of the MED compartment will be a viable option for monitoring the progression of knee OA. Furthermore, automatic segmentation based on nnUNet demonstrates its ability to replace semi-automatic segmentation performed by an expert.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100419"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543817","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":"AUTOMATIC HIGH-RESOLUTION CARTILAGE THICKNESS MAPPING AND SCORING: EXTENSION TO STANDARD CLINICAL MRI - DATA FROM THE LAUSANNE KNEE STUDY (LKS)","authors":"Paul Margain,&nbsp;Patrick Omoumi,&nbsp;Julien Favre","doi":"10.1016/j.ostima.2026.100418","DOIUrl":"10.1016/j.ostima.2026.100418","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;A fully automated MRI-based framework was recently developed to assess femorotibial cartilage damage severity using anatomically standardized cartilage thickness maps (CTh-Maps) and an associated pattern-based severity score (CTh-Score, ranging from 0, indicating healthy cartilage, to 100, indicating end-stage osteoarthritis). The framework showed excellent reproducibility, strong agreement with expert MRI cartilage-loss assessment, and sensitivity to structural osteoarthritis progression. It was developed and validated using high-resolution DESS MRI, which is not routinely acquired in clinical practice. Standard clinical knee MRI more commonly uses tri-planar 2D fat-suppressed intermediate-weighted fast spin echo (FSE FS IW) sequences, which have lower through-plane resolution and reduced cartilage-bone contrast (Figure 1). Translating CTh-Maps and CTh-Score computation to standard clinical MRI would support routine use.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To adapt the framework to routine clinical tri-planar 2D FSE FS IW MRI and assess whether clinical MRI-derived CTh-Maps and CTh-Scores agree with DESS-based reference measurements.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;This study included 430 participants of the Lausanne Knee Study (LKS), with one randomly selected knee per participant. Each underwent 3D DESS MRI, with 0.63 mm isotropic voxels, and tri-planar 2D FSE FS IW MRI, with through-plane voxel dimensions 3.3 mm. DESS cartilage segmentations generated by expert musculoskeletal radiologists served as reference standards. A 3D U-Net implemented with nnU-Net was trained to segment cartilage from interpolated and concatenated sagittal, coronal, and axial FSE FS IW images using DESS-derived segmentations as ground truth. Training included 352 subjects, with 78 held out for testing. CTh-Map accuracy, precision, and agreement were assessed using mean thickness difference, standard deviation of thickness difference, and voxel-wise ICC, respectively. CTh-Scores from FSE FS IW- and DESS-based maps were compared using ICC and Bland-Altman limits of agreement. DESS test-retest reproducibility was assessed in 11 independent knees.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;The cohort included 192 males and 238 females, with a mean age of 47.8 ± 16.2 years old . In the DESS test-retest analysis, CTh-Maps showed an accuracy of −0.01 ± 0.02 mm, precision of 0.21 ± 0.02 mm, and excellent reliability, with an ICC of 0.96 ± 0.02. In the 78-subject test set, FSE FS IW-based CTh-Maps closely agreed with DESS-based reference maps, with an accuracy of 0.04 ± 0.08 mm, precision of 0.21 ± 0.11 mm, and ICC of 0.95 ± 0.02. CTh-Scores computed from FSE FS IW- and DESS-based maps also showed excellent agreement, with an ICC of 0.91. Bland-Altman analysis showed a bias of −1.8 points on the 0–100 CTh-Score scale, with 95% limits of agreement from −7.5 to 5.0.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;CONCLUSION&lt;/h3&gt;&lt;div&gt;CTh-Maps and CTh-Scores can be reliably d","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100418"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543808","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 CAM-TYPE FEMOROACETABULAR IMPINGEMENT OF THE PROXIMAL FEMUR USING 3D MRI","authors":"O. Cigdem,&nbsp;E. Montin,&nbsp;S. Bin Ghouth,&nbsp;J. Lee,&nbsp;V. Mazzoli,&nbsp;R. Lattanzi","doi":"10.1016/j.ostima.2026.100402","DOIUrl":"10.1016/j.ostima.2026.100402","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;Femoroacetabular impingement (FAI) is a hip disorder involving bony abnormalities that cause collisions of the femoral head–neck junction with the acetabular rim, leading to labral tears, cartilage damage, and early-onset osteoarthritis. Cam FAI is characterized by a focal bony prominence at the anterolateral head–neck junction, while Pincer FAI involves acetabular over-coverage. Current clinical assessment relies on 2D measurements such as the alpha angle for Cam FAI, which may not fully capture the 3D nature of these deformities. Statistical shape modeling (SSM) offers a data-driven approach to characterize complex 3D morphological differences between healthy and pathological anatomy.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To develop a principal component analysis (PCA)-based SSM pipeline for 3D MRI to identify and characterize shape features associated with Cam and Pincer FAI. This preliminary analysis focuses on Cam morphology of the femoral head–neck region using a balanced subset of the full cohort.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;This study was approved by the IRB. We selected 10 asymptomatic controls (5F; age 32.0±4.9 years) and 41 patients with unilateral mixed-type FAI confirmed during arthroscopy (22F; age 36.2±7.9 years). All subjects received a 3T MRI scan (preoperative for patients) including a 3D VIBE sequence with Dixon water-fat separation of the entire pelvis (TR=10ms, TE=2.4/3.7ms, matrix=320 × 320 × 160, 1.0mm isotropic). In this preliminary study, we analyzed only the symptomatic hip that underwent arthroscopy, matching the side for controls. The femur was manually segmented with assistance from a musculoskeletal radiologist. Surface meshes of the femoral head-neck region were generated using discrete marching cubes, followed by windowed sinc and Taubin smoothing. The SSM pipeline consisted of three stages: (1) RBF-based non-rigid registration to a reference subject and alignment of 10 control meshes to obtain a mean shape representing normal anatomy; (2) a combined PCA model trained on all 20 subjects registered to the control mean; (3) projection onto PCA space, where patient and control weight distributions were compared using Welch's t-tests, Cohen's d effect sizes, FDR correction, and power analysis. Shape modes were visualized as signed surface displacement maps at ±2SD.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Five PCs explained 92.6% of total shape variance (Table 1). PC1-PC3 (86.0%) captured overall femoral head size, head shape, and neck contour variation. PC4 (4.0%) captured a focal outward displacement (up to 2.9mm) at the anterolateral head-neck junction consistent with cam morphology (d=0.71, p=0.132). Figure 1 shows the PC4 signed surface displacement maps at -2SD, mean, and +2SD, where the -2SD extreme reveals a clearly localized outward bump (red) at the head-neck junction corresponding to the cam deformity, while the +2SD extreme shows a smooth contour. FAI patients had lower ","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100402"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148557920","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":"AUTOMATED KNEE CARTILAGE SEGMENTATION FOR T2 QUANTIFICATION USING HIGH-RESOLUTION MRI: VALIDATION IN THE NACOX ACL INJURY COHORT","authors":"L. Hassanlou,&nbsp;V. Casula,&nbsp;B.E. Tajik,&nbsp;H. Gauffin,&nbsp;M.T. Nieminen,&nbsp;S. Saarakkala,&nbsp;J. Kvist,&nbsp;M. Englund,&nbsp;E. Panfilov","doi":"10.1016/j.ostima.2026.100411","DOIUrl":"10.1016/j.ostima.2026.100411","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;T2 mapping enables early detection and monitoring of cartilage degeneration following ACL injury, yet reliable quantification requires accurate segmentation of cartilage from high-resolution MRI data. Manual segmentation of cartilage is time-consuming and evaluator-dependent, limiting its use in large cohort studies. Deep learning provides a scalable alternative, but validation using high-resolution MRI data from post-traumatic OA cohorts is limited.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;1) To validate automated deep learning-based cartilage segmentation as a substitute for manual segmentation in T2 quantification using high-resolution clinical MRI data from the NACOX ACL injury cohort. 2) To compare segmentation performance and failure patterns between the second echo image of the multi-echo train and the T2 parametric map training inputs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;From the NACOX cohort, a multicenter study of patients aged 15–40 years with acute ACL injury, we used baseline data from 129 patients enrolled at Linköping University Hospital, Sweden. After excluding patients with missing MRI scans, image artifacts, and missing annotations, 117 patients (231 knees) were included. MRI was performed on a Philips Ingenia 3T scanner using a multi-echo spin-echo sequence (TE=10-80ms, 8 echoes; in-plane resolution 0.29 × 0.29mm; slice thickness 3mm; 26 sagittal slices per volume). T2 maps were derived via monoexponential fitting, including values in the range of 10-90ms. Manual segmentation of femoral and tibial cartilage (FC, TC) was performed on 6 slices per scan, centered within the medial and lateral compartments (3 slices each). Segmentations were performed by a trained observer supervised by an expert with 8 years of experience (mean intrareader RMS-CV: 2.0% FC, 2.4% TC). The dataset was split into 93 patients (183 scans) for training and 24 patients (46 scans) as a hold-out test set. We trained an ensemble of Swin-UNETR models (Hatamizadeh et al. Lect Notes Comput Sci 12962 2022) using 5-fold cross-validation. Two input data settings were compared: the second echo image of the echo train (\"Echo 2\", TE=20ms) and T2 parametric maps (Ibrahim et al. Osteoarthritis Cartilage 33(S1) 2025).We assessed segmentation accuracy using Dice score, average symmetric surface distance (ASSD), and 95th-percentile Hausdorff distance (HD95), and evaluated downstream validity via Bland-Altman agreement analysis of average T2 values between automated and manual segmentations tissue-wise. Unless otherwise stated, values are reported as mean ± SD.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;Both input datasets achieved high segmentation accuracy on the test set (Table 1). “Echo 2” and T2 map showed similar Dice scores (FC: 0.887±0.022 vs 0.894±0.024; TC: 0.899±0.019 vs 0.903±0.018) and were comparable with T2 agreement (FC: 0.22ms [95% LoA: −0.47, 0.92ms] vs 0.07ms [95% LoA: −0.60, 0.74ms]; TC: −0.09ms [95% LoA: −1.08, 0.90ms] vs 0.14ms","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100411"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543596","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":"MOVING BEYOND THE KELLGREN–LAWRENCE GRADE: A DEEP-LEARNING-BASED RADIOMICS SCORE FOR PREDICTING KNEE REPLACEMENT RISK","authors":"T. Jiang,&nbsp;L.C. Chan,&nbsp;W. Wang,&nbsp;S.H. Lau,&nbsp;P.K. Chan,&nbsp;H. Chen,&nbsp;D.J. Hunter,&nbsp;T.L. Vincent,&nbsp;F.E. Watt,&nbsp;C. Wen","doi":"10.1016/j.ostima.2026.100399","DOIUrl":"10.1016/j.ostima.2026.100399","url":null,"abstract":"<div><h3>INTRODUCTION</h3><div>KL grading is widely used for radiographic knee osteoarthritis assessment but remains categorical, reader-dependent, and limited in prognostic granularity. An automated continuous imaging biomarker from standard radiographs may improve knee replacement (KR) risk prediction.</div></div><div><h3>OBJECTIVE</h3><div>To develop a deep-learning-based radiomics score (DR Score) from posteroanterior knee radiographs and compare its prognostic performance with KL grade for KR risk prediction.</div></div><div><h3>METHODS</h3><div>We analyzed baseline standing posteroanterior knee radiographs from MOST and OAI for model development and internal testing, including 6,578 participants and 12,905 knees. External testing used MenTOR and KICK, including 135 participants. An automated deep-learning pipeline localized the knee joint, extracted patch-level radiographic features, aggregated informative regions using attention-based multiple-instance learning, and generated a continuous 0–4 DR Score using DeepSurv survival modelling. The outcome was time to KR in the US cohorts and virtual KR in the UK cohorts. Predictive performance was assessed using C-index and integrated time-dependent AUC, with bootstrap confidence intervals and permutation testing.</div></div><div><h3>RESULTS</h3><div>In internal testing, the DR Score achieved a C-index of 0.849 (95% CI: 0.826–0.871), higher than KL grade alone (0.821, 95% CI: 0.795–0.851; p = 0.037). At 108 months, the DR Score also showed higher iAUC than KL grade (0.883, 95% CI: 0.859–0.904 vs 0.856, 95% CI: 0.830–0.880; p = 0.019). Combining DR Score with KL grade further improved performance, with a C-index of 0.863 (95% CI: 0.838–0.889) and 108-month iAUC of 0.903 (95% CI: 0.878–0.923). In external testing, the DR Score showed similar discrimination to KL grade, with C-indices of 0.755 and 0.751, respectively. The combined model achieved the highest external C-index of 0.804. The DR Score was moderately correlated with KL grade (ρ = 0.696, p &lt; 0.001) and remained independently associated with KR after adjustment for age, sex, BMI, and KL grade.</div></div><div><h3>CONCLUSION</h3><div>The DR Score is an automated, continuous radiographic biomarker for KR risk prediction. It provides prognostic information complementary to KL grade and may support scalable risk stratification in knee osteoarthritis.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100399"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543599","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}