Search

BackgroundObesity influences the development of osteoarthritis via low-grade inflammation. Progression of local inflammation (= synovitis) increased with weight gain in overweight and obese women compared to stable weight. Synovitis could be associated with subcutaneous fat (SCF) around the knee. Purpose of the study was to investigate the effect of weight loss on synovitis progression and to assess whether SCF around the knee mediates the relationship between weight loss and synovitis progression.MethodsWe included 234 overweight and obese participants (body mass index [BMI] ≥ 25 kg/m2) from the Osteoarthritis Initiative (OAI) with > 10% weight loss (n = 117) or stable overweight (

ObjectiveTo assess (i) the impact of changes in body weight on changes in joint-adjacent subcutaneous fat (SCF) and cartilage thickness over 4 years and (ii) the relation between changes in joint-adjacent SCF and knee cartilage thickness.DesignIndividuals from the Osteoarthritis Initiative (total=399) with > 10% weight gain (n=100) and > 10% weight loss (n=100) over 4 years were compared to a matched control cohort with less than 3% change in weight (n=199). 3.0T Magnetic Resonance Imaging (MRI) of the right knee was performed at baseline and after 4 years to quantify joint-adjacent SCF and cartilage thickness. Linear regression models were used to evaluate the associations between the (i) weight change group and 4-year changes in both knee SCF and cartilage thickness, and (ii) 4-year changes in knee SCF and in cartilage thickness. Analyses were adjusted for age, sex, baseline body mass index (BMI), tibial diameter (and weight change group in analysis (ii)).ResultsIndividuals who lost weight over 4-years had significantly less joint-adjacent SCF (beta range, medial/lateral joint sides: 2.2-4.2 mm, p

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.

PurposeClinical management of disc degeneration in patients with chronic low back pain (cLBP) is hampered by the challenge of distinguishing pathologic changes relating to pain from physiologic changes related to aging. The goal of this study was to use imaging biomarkers of disc biochemical composition to distinguish degenerative changes associated with cLBP from normal aging.MethodsT1ρ MRI data were acquired from 133 prospectively enrolled subjects for this observational study (80 cLBP, 53 controls; mean ± SD age = 43.9 ± 13.4 years; 61 females, 72 males). The mean T1ρ relaxation time in the nucleus pulposus (NP-T1ρ; n = 650 discs) was used as a quantitative biomarker of disc biochemical composition. Linear regression was used to assess associations between NP-T1ρ and age, sex, spinal level, and study group, and their interactions.ResultsNP-T1ρ values were lower in cLBP patients than controls (70.8 ± 22.8 vs. 76.4 ± 22.2 ms, p = 0.009). Group differences were largest at L5-S1 (ΔT1ρcLBP-control = -11.3 ms, p

ObjectiveTo assess the effects of weight loss and weight gain on hip and knee radiographic changes, pain, and joint replacement over 4 years.MethodsParticipants (n = 2,752) from the Osteoarthritis Initiative were classified as those with weight gain (more than 5% gain), weight loss (more than -5% loss), or as controls (-3% to 3% change) over 4 years. Generalized estimating equations (adjusted for age, sex, and body mass index) were used to assess the relationship between the weight-change group and 4-year changes in knee radiographic osteoarthritis (OA) (Kellgren/Lawrence [K/L] grade), hip OA (Croft summary grade), joint space narrowing (JSN), and joint pain.ResultsFor radiographic knee OA, weight loss was associated with significantly lower odds of K/L grade worsening over 4 years (odds ratio [OR] 0.69 [95% confidence interval (95% CI) 0.53-0.91], P = 0.009), and weight gain was significantly associated with higher odds of medial knee JSN (OR 1.29 [95% CI 1.01-1.64], P = 0.038) compared to controls. For knee pain, weight loss was significantly associated with knee pain resolution over 4 years (OR 1.40 [95% CI 1.06-1.86], P = 0.019) while weight gain was associated with knee pain development (OR 1.34 [95% CI 1.08-1.67], P = 0.009) compared to controls. For all hip outcomes, no significant associations (P > 0.05) were found with weight-change groups. The associations between the weight-change group and total hip or total knee replacement were not significant (P > 0.05).ConclusionThis large, longitudinal study (n = 2,752 with 4-year follow-up) suggests that weight loss may protect against, and weight gain may exacerbate, radiographic and symptomatic knee OA, while weight change (at a 5% threshold) does not have significant effects on hip OA.

BackgroundSynovial inflammation is a risk factor for osteoarthritis (OA). But to date, there is limited information on how inflammation impacts progression of knee OA.PurposeTo investigate how sustained synovitis, assessed with semi-quantitative magnetic resonance imaging (MRI) scores, impacts progression of knee degenerative changes over 4 years.Study typeRetrospective cohort study.SubjectsIn 249 participants (N = 132 women [53%]), from the Osteoarthritis Initiative (OAI) two definitions for synovitis were used resulting in two groups of participants with sustained synovitis at baseline, 2-year, and 4-year follow-up (N = 80 and N = 132), and two groups without synovitis at all three time points (N = 81 and N = 47).Field strength/sequence3 T intermediate-weighted (IW) turbo spin-echo (TSE) sequence and three-dimensional (3D) dual-echo steady-state (DESS) sequence.AssessmentSynovitis was scored semi-quantitatively using the Anterior Cruciate Ligament Osteoarthritis Score (ACLOAS), MRI Osteoarthritis Knee Score (MOAKS), and synovial proliferation score (SPS). Two MRI-based definitions of synovitis were used: (i) score ≥2 based on cumulative score of MOAKS and ACLOAS, and (ii) score ≥3 based on the cumulative score of ACLOAS, MOAKS, and SPS. Changes in structural abnormalities from baseline to year 4 measured using the whole-organ MRI score (WORMS) were defined as outcomes.Statistical testsLinear regression models were used to compare the differences in longitudinal changes in WORMS scores between participants with and without sustained synovitis for each definition of sustained synovitis. A P-value of

Background: Gadolinium (Gd)-enhanced Magnetic Resonance Imaging (MRI) is crucial in several applications, including oncology, cardiac imaging, and musculoskeletal inflammatory imaging. One use case is rheumatoid arthritis (RA), a widespread autoimmune condition for which Gd MRI is crucial in imaging synovial joint inflammation, but Gd administration has well-documented safety concerns. As such, algorithms that could synthetically generate post-contrast peripheral joint MR images from non-contrast MR sequences would have immense clinical utility. Moreover, while such algorithms have been investigated for other anatomies, they are largely unexplored for musculoskeletal applications such as RA, and efforts to understand trained models and improve trust in their predictions have been limited in medical imaging. Methods: A dataset of 27 RA patients was used to train algorithms that synthetically generated post-Gd IDEAL wrist coronal T1-weighted scans from pre-contrast scans. UNets and PatchGANs were trained, leveraging an anomaly-weighted L1 loss and global generative adversarial network (GAN) loss for the PatchGAN. Occlusion and uncertainty maps were also generated to understand model performance. Results: UNet synthetic post-contrast images exhibited stronger normalized root mean square error (nRMSE) than PatchGAN in full volumes and the wrist, but PatchGAN outperformed UNet in synovial joints (UNet nRMSEs: volume = 6.29 ± 0.88, wrist = 4.36 ± 0.60, synovial = 26.18 ± 7.45; PatchGAN nRMSEs: volume = 6.72 ± 0.81, wrist = 6.07 ± 1.22, synovial = 23.14 ± 7.37; n = 7). Occlusion maps showed that synovial joints made substantial contributions to PatchGAN and UNet predictions, while uncertainty maps showed that PatchGAN predictions were more confident within those joints. Conclusions: Both pipelines showed promising performance in synthesizing post-contrast images, but PatchGAN performance was stronger and more confident within synovial joints, where an algorithm like this would have maximal clinical utility. Image synthesis approaches are therefore promising for RA and synthetic inflammatory imaging.

BackgroundTo assess the compound effects of BMI and sustained depressive symptoms on changes in knee structure, cartilage composition, and knee pain over 4 years using statistical interaction analyses.MethodsOne thousand eight hundred forty-four individuals from the Osteoarthritis Initiative Database were analyzed at baseline and 4-year follow-up. Individuals were categorized according to their BMI and presence of depressive symptoms (based on the Center for Epidemiological Studies Depression Scale (threshold≥16)) at baseline and 4-year follow-up. 3 T MRI was used to quantify knee cartilage T2 over 4 years, while radiographs were used to assess joint space narrowing (JSN). Mixed effects models examined the effect of BMI-depressive symptoms interactions on outcomes of cartilage T2, JSN, and knee pain over 4-years.ResultsThe BMI-depressive symptoms interaction was significantly associated with knee pain (p

BackgroundIt is unknown if football players with femoroacetabular impingement (FAI) syndrome report worse burden than those with other causes of hip/groin pain, and to what extent this is mediated by cartilage defects and labral tears.HypothesisFootball players with FAI syndrome would report worse burden than other symptomatic players, with the effect partially mediated by cartilage defects and/or labral tears.Study designCross-sectional study.Level of evidenceLevel 4.MethodsFootball (soccer and Australian football) players (n = 165; 35 women) with hip/groin pain (≥6 months and positive flexion-adduction-internal rotation test) were recruited. Participants completed 2 patient-reported outcome measures (PROMs; the International Hip Outcome Tool-33 [iHOT-33] and Copenhagen Hip and Groin Outcome Score [HAGOS]) and underwent hip radiographs and magnetic resonance imaging (MRI). FAI syndrome was determined to be present when cam and/or pincer morphology were present. Cartilage defects and labral tears were graded as present or absent using MRI. Linear regression models investigated relationships between FAI syndrome (dichotomous independent variable) and PROM scores (dependent variables). Mediation analyses investigated the effect of cartilage defects and labral tears on these relationships.ResultsFAI syndrome was not related to PROM scores (unadjusted b values ranged from -4.693 (P = 0.23) to 0.337 (P = 0.93)) and cartilage defects and/or labral tears did not mediate its effect (P = 0.22-0.97).ConclusionFootball players with FAI syndrome did not report worse burden than those with other causes of hip/groin pain. Cartilage defects and/or labral tears did not explain the effect of FAI syndrome on reported burden.Clinical relevanceFAI syndrome, cartilage defects, and labral tears were prevalent but unrelated to reported burden in symptomatic football players.

BackgroundParaspinal musculature (PSM) is increasingly recognized as a contributor to low back pain (LBP), but with conventional MRI sequences, assessment is limited. Chemical shift encoding-based water-fat MRI (CSE-MRI) enables the measurement of PSM fat fraction (FF), which may assist investigations of chronic LBP.PurposeTo investigate associations between PSM parameters from conventional MRI and CSE-MRI and between PSM parameters and pain.Study typeProspective, cross-sectional.PopulationEighty-four adults with chronic LBP (44.6 ± 13.4 years; 48 males).Field strength/sequence3-T, T1-weighted fast spin-echo and iterative decomposition of water and fat with echo asymmetry and least squares estimation sequences.AssessmentT1-weighted images for Goutallier classification (GC), muscle volume, lumbar indentation value, and muscle-fat index, CSE-MRI for FF extraction (L1/2-L5/S1). Pain was self-reported using a visual analogue scale (VAS). Intra- and/or interreader agreement was assessed for MRI-derived parameters.Statistical testsMixed-effects and linear regression models to 1) assess relationships between PSM parameters (entire cohort and subgroup with GC grades 0 and 1; statistical significance α = 0.0025) and 2) evaluate associations of PSM parameters with pain (α = 0.05). Intraclass correlation coefficients (ICCs) for intra- and/or interreader agreement.ResultsThe FF showed excellent intra- and interreader agreement (ICC range: 0.97-0.99) and was significantly associated with GC at all spinal levels. Subgroup analysis suggested that early/subtle changes in PSM are detectable with FF but not with GC, given the absence of significant associations between FF and GC (P-value range: 0.036 at L5/S1 to 0.784 at L2/L3). Averaged over all spinal levels, FF and GC were significantly associated with VAS scores.Data conclusionIn the absence of FF, GC may be the best surrogate for PSM quality. Given the ability of CSE-MRI to detect muscle alterations at early stages of PSM degeneration, this technique may have potential for further investigations of the role of PSM in chronic LBP.Level of evidence2 TECHNICAL EFFICACY STAGE: 2.

The aims of this case-control study were to: (1) Identify cartilage locations and volumes at risk of osteoarthritis (OA) using subject-specific finite element (FE) models; (2) Quantify the relationships between the simulated biomechanical parameters and T2 and T1ρ relaxation times of magnetic resonance imaging (MRI). We created subject-specific FE models for seven patients with anterior cruciate ligament (ACL) reconstruction and six controls based on a previous proof-of-concept study. We identified locations and cartilage volumes susceptible to OA, based on maximum principal stresses and absolute maximum shear strains in cartilage exceeding thresholds of 7 MPa and 32%, respectively. The locations and volumes susceptible to OA were compared qualitatively and quantitatively against 2-year longitudinal changes in T2 and T1ρ relaxation times. The degeneration volumes predicted by the FE models, based on excessive maximum principal stresses, were significantly correlated (r = 0.711, p

Excessive tissue deformation near cartilage lesions and acute inflammation within the knee joint after anterior cruciate ligament (ACL) rupture and reconstruction surgery accelerate the loss of fixed charge density (FCD) and subsequent cartilage tissue degeneration. Here, we show how biomechanical and biochemical degradation pathways can predict FCD loss using a patient-specific finite element model of an ACL reconstructed knee joint exhibiting a chondral lesion. Biomechanical degradation was based on the excessive maximum shear strains that may result in cell apoptosis, while biochemical degradation was driven by the diffusion of pro-inflammatory cytokines. We found that the biomechanical model was able to predict substantial localized FCD loss near the lesion and on the medial areas of the lateral tibial cartilage. In turn, the biochemical model predicted FCD loss all around the lesion and at intact areas; the highest FCD loss was at the cartilage-synovial fluid-interface and decreased toward the deeper zones. Interestingly, simulating a downturn of an acute inflammatory response by reducing the cytokine concentration exponentially over time in synovial fluid led to a partial recovery of FCD content in the cartilage. Our novel numerical approach suggests that in vivo FCD loss can be estimated in injured cartilage following ACL injury and reconstruction. Our novel modeling platform can benefit the prediction of PTOA progression and the development of treatment interventions such as disease-modifying drug testing and rehabilitation strategies.

PurposeThe composition of the subchondral bone marrow and cartilage endplate (CEP) could affect intervertebral disc health by influencing vertebral perfusion and nutrient diffusion. However, the relative contributions of these factors to disc degeneration in patients with chronic low back pain (cLBP) have not been quantified. The goal of this study was to use compositional biomarkers derived from quantitative MRI to establish how CEP composition (surrogate for permeability) and vertebral bone marrow fat fraction (BMFF, surrogate for perfusion) relate to disc degeneration.MethodsMRI data from 60 patients with cLBP were included in this prospective observational study (28 female, 32 male; age = 40.0 ± 11.9 years, 19-65 [mean ± SD, min-max]). Ultra-short echo-time MRI was used to calculate CEP T2* relaxation times (reflecting biochemical composition), water-fat MRI was used to calculate vertebral BMFF, and T1ρ MRI was used to calculate T1ρ relaxation times in the nucleus pulposus (NP T1ρ, reflecting proteoglycan content and degenerative grade). Univariate linear regression was used to assess the independent effects of CEP T2* and vertebral BMFF on NP T1ρ. Mixed effects multivariable linear regression accounting for age, sex, and BMI was used to assess the combined relationship between variables.ResultsCEP T2* and vertebral BMFF were independently associated with NP T1ρ (p = 0.003 and 0.0001, respectively). After adjusting for age, sex, and BMI, NP T1ρ remained significantly associated with CEP T2* (p = 0.0001) but not vertebral BMFF (p = 0.43).ConclusionPoor CEP composition plays a significant role in disc degeneration severity and can affect disc health both with and without deficits in vertebral perfusion.

BackgroundModic changes (MCs) are the most prevalent classification system for describing magnetic resonance imaging (MRI) signal intensity changes in the vertebrae. However, there is a growing need for novel quantitative and standardized methods of characterizing these anomalies, particularly for lesions of transitional or mixed nature, due to the lack of conclusive evidence of their associations with low back pain. This retrospective imaging study aims to develop an interpretable deep learning-based detection tool for voxel-wise mapping of MCs.MethodsSeventy-five lumbar spine MRI exams that presented with acute-to-chronic low back pain, radiculopathy, and other symptoms of the lumbar spine were enrolled. The pipeline consists of two deep convolutional neural networks to generate an interpretable voxel-wise Modic map. First, an autoencoder was trained to segment vertebral bodies from T1-weighted sagittal lumbar spine images. Next, two radiologists segmented and labeled MCs from a combined T1- and T2-weighted assessment to serve as ground truth for training a second autoencoder that performs segmentation of MCs. The voxels in the detected regions were then categorized to the appropriate Modic type using a rule-based signal intensity algorithm. Post hoc, three radiologists independently graded a second dataset with the aid of the model predictions in an artificial (AI)-assisted experiment.ResultsThe model successfully identified the presence of changes in 85.7% of samples in the unseen test set with a sensitivity of 0.71 (±0.072), specificity of 0.95 (±0.022), and Cohen's kappa score of 0.63. In the AI-assisted experiment, the agreement between the junior radiologist and the senior neuroradiologist significantly improved from Cohen's kappa score of 0.52 to 0.58 (p

PurposeTo assess the correlation between changes in hip capsule morphology with improvements in patient-reported outcome (PRO) scores after arthroscopic surgery for femoroacetabular impingement syndrome (FAIS) using the periportal capsulotomy technique.MethodsTwenty-eight patients with cam morphology FAIS (without arthritis, dysplasia, or hypermobility) were prospectively enrolled before arthroscopic labral repair and femoroplasty through periportal capsulotomy (anterolateral/midanterior portals) without closure. Patients completed the Hip Disability and Osteoarthritis Outcomes Score (HOOS) and had nonarthrographic 3T magnetic resonance imaging (MRI) scans of the affected hip before and 1 year after surgery. Anterior capsule thickness, posterior capsule thickness, anterior-posterior capsule thickness ratio, and proximal-distal anterior capsule thickness ratio were measured on axial-oblique MRI sequences. Pearson correlation coefficients were calculated to determine the association between hip capsule morphology and PRO scores.ResultsPostoperative imaging showed that for all 28 patients (12 female), labral repairs and capsulotomies had healed within 1 year of surgery. Analysis revealed postoperative decreases in anterior hip capsule thickness (1395.4 ± 508.4 mm3 vs 1758.4 ± 487.9 mm3; P = .003) and anterior-posterior capsule thickness ratio (0.92 ± 0.33 vs 1.12 ± 0.38; P = .02). Higher preoperative anterior-posterior capsule thickness ratio correlated with lower preoperative scores for HOOS pain (R = -0.43; P = .02), activities of daily living (ADL) (R = -0.43; P = .02), and sport (R = -0.38; P = .04). Greater decrease from preoperative to postoperative anterior-posterior capsule thickness ratio correlated with greater improvement for HOOS pain (R = -0.40; P = .04), ADL (R = -0.45; P = .02), and sport (R = -0.46; P = .02).ConclusionsPeriportal capsulotomy without closure demonstrates capsule healing by 1 year after arthroscopic FAIS treatment. Changes in hip capsule morphology including decreased anterior-posterior capsule thickness ratio after surgery may be correlated with improvements in patient pain, function, and ability to return to sports.Level of evidenceLevel II, prospective cohort study.

PurposeOsteoporosis is a highly prevalent skeletal disease that frequently entails vertebral fractures. Areal bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the reference standard, but has well-known limitations. Texture analysis can provide surrogate markers of tissue microstructure based on computed tomography (CT) or magnetic resonance imaging (MRI) data of the spine, thus potentially improving fracture risk estimation beyond areal BMD. However, it is largely unknown whether MRI-derived texture analysis can predict volumetric BMD (vBMD), or whether a model incorporating texture analysis based on CT and MRI may be capable of differentiating between patients with and without osteoporotic vertebral fractures.Materials and methodsTwenty-six patients (15 females, median age: 73 years, 11 patients showing at least one osteoporotic vertebral fracture) who had CT and 3-Tesla chemical shift encoding-based water-fat MRI (CSE-MRI) available were analyzed. In total, 171 vertebral bodies of the thoracolumbar spine were segmented using an automatic convolutional neural network (CNN)-based framework, followed by extraction of integral and trabecular vBMD using CT data. For CSE-MRI, manual segmentation of vertebral bodies and consecutive extraction of the mean proton density fat fraction (PDFF) and T2* was performed. First-order, second-order, and higher-order texture features were derived from texture analysis using CT and CSE-MRI data. Stepwise multivariate linear regression models were computed using integral vBMD and fracture status as dependent variables.ResultsPatients with osteoporotic vertebral fractures showed significantly lower integral and trabecular vBMD when compared to patients without fractures (p

ObjectiveTo assess differences in biochemical composition of the deep cartilage layer in subjects with type 2 diabetes mellitus (T2DM) and nondiabetic controls using UTE (ultra-short echo time) T2* mapping and to investigate the association of vascular health and UTE T2* measurements.DesignTen subjects with T2DM matched for age, sex, and body mass index with 10 nondiabetic controls. A 3D UTE sequence with 6 echo times was acquired using 3T magnetic resonance imaging of the knee. For UTE T2* analysis, the deep cartilage layer was segmented and analyzed in 5 compartments (patella, medial, and lateral femur and tibia). The ankle brachial index (ABI) was obtained in all subjects. Linear regression analyses were used to assess associations of T2DM and UTE T2* relaxation times and the associations of ABI measurements and UTE measurements.ResultsCompared with nondiabetic controls, T2DM subjects had significantly lower mean T2*-UTE in the patella (mean difference 4.87 ms; 95% confidence interval [CI] 1.09-8.65; P = 0.015), the lateral tibia (mean difference 2.26 ms; 95% CI 0.06-4.45; P = 0.045), and the lateral femur (mean difference 4.96 ms; 95% CI 0.19-9.73; P = 0.043). Independent of diabetic status, subjects with higher ABI values, indicating better vascular health, had higher T2*-UTE of the patella (coefficient 15.2; 95% CI 3.3-21.4; P = 0.017), the medial tibia (coefficient 9.8; 95% CI 1.0-18.6; P = 0.031), and the lateral femur (coefficient 18.8; 95% CI 3.3-34.3; P = 0.021).ConclusionsT2*-UTE measurements of the deep cartilage layer were consistently lower in subjects with T2DM and in subjects with impaired vascular health, likely indicating increased mineralization of this layer.

BackgroundOpioids are frequently prescribed for pain control in knee osteoarthritis patients, despite recommendations by current guidelines. Previous studies have investigated the chondrotoxicity of different opioid subtypes. However, the impact opioids may have on progression of osteoarthritis in vivo remains unknown. The aim of this study was thus to describe the associations between opioid use and knee structural changes and clinical outcomes, over 4 years.MethodsParticipants with baseline opioid use (n=181) and who continued use for ≥1 year between baseline and 4-year follow-up (n=79) were included from the Osteoarthritis Initiative cohort and frequency matched with non-users (controls) (1:2). Whole-Organ Magnetic Resonance Imaging Scores (WORMS) were obtained, including a total summation score (WORMS total, range 0-96) and subscores for cartilage (0-36), menisci (0-24), and bone marrow abnormalities and subchondral cyst-like lesions (0-18, respectively). Knee Injury Osteoarthritis Outcomes score (KOOS) symptoms, quality of life (QOL), and pain were also obtained at baseline and follow-up (range 0-100; lower scores indicate worse outcomes). Using linear regression models, associations between baseline and longitudinal findings were investigated. As pain may modify observations, a sensitivity analysis was performed for longitudinal findings. All analyses were adjusted for sex, BMI, age, race, and Kellgren-Lawrence grade.ResultsOpioid users had greater structural degeneration at baseline (WORMS total: Coef. [95% CI], P; 7.1 [5.5, 8.8],

ObjectiveTo identify joint structural risk factors, measured using quantitative compositional and semiquantitative magnetic resonance imaging (MRI) scoring, associated with the development of accelerated knee osteoarthritis (AKOA) compared with a more normal rate of knee osteoarthritis (OA) development.DesignFrom the Osteoarthritis Initiative we selected knees with no radiographic OA (Kellgren-Lawrence grade [KL] 0/1) that developed advanced-stage OA (KL 3/4; AKOA) within a 4-year timeframe and a comparison group with a more normal rate of OA development (KL 0/1 to KL 2 in 4 years). MRIs at the beginning of the 4-year timeframe were assessed for cartilage T2 values and structural abnormalities using a modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Associations of MRI findings with AKOA versus normal OA were assessed using multivariable logistic regression models.ResultsA total of 106 AKOA and 168 subjects with normal OA development were included. Mean cartilage T2 values were not significantly associated with AKOA (odds ratio [OR] 1.06; 95% confidence interval [CI] 0.82-1.36). Risk factors for AKOA development included higher meniscus maximum scores (OR 1.37; 95% CI 1.11-1.68), presence of meniscal extrusion (OR 6.30; 95% CI 2.57-15.49), presence of root tears (OR 4.64; 95% CI 1.61-13.34), and higher medial tibia cartilage lesion scores (OR 1.96; 95% CI 1.19-3.24).ConclusionsWe identified meniscal damage, especially meniscal extrusion and meniscal root tears as risk factors for AKOA development. These findings contribute to identifying subjects at risk of AKOA at an early stage when preventative measures targeting modifiable risk factors such as meniscal repair surgery could still be effective.

PurposePatients with non-Hodgkin lymphoma (NHL) have a median age of 67, with 70% surviving over 5 years. Chemotherapy for aggressive NHL includes cyclophosphamide, anthracycline, and high doses of corticosteroids, which can impair bone health. By reviewing clinical characteristics and standard-of-care CT scans, we evaluate the prevalence and incidence of fractures and the clinical correlates of fractures in patients treated for aggressive B-cell NHL.MethodsWe retrospectively reviewed patients seen at the University of California San Francisco lymphoma clinic from January 1, 2016, to March 31, 2017 who had (1) aggressive B-cell NHL, (2) received first-line therapy with R-CHOP-like regimens, and had (3) CT scans pre- and post-treatment available for review. Associations between clinical variables and vertebral, rib, and pelvic fracture outcomes were assessed, and multivariate logistic regression models were used to identify predictors of prevalent and incident fractures.ResultsWe identified 162 patients who met the inclusion criteria. Median age at diagnosis was 60 years. Of the 162 patients, 38 patients (28%) had prevalent fractures prior to receiving chemotherapy. Within 1 year after treatment, 16 patients (10%) developed new fractures. Having a prevalent fracture strongly predicted developing a new fracture after treatment, with incident fractures occurring in 12 of 38 patients with prevalent fractures versus 4 of 124 without prevalent fractures (odds ratio 10.45, p

Clinicians often examine movement patterns to design hip osteoarthritis (OA) interventions, yet traditional biomechanical analyses only report a single timepoint. Multivariate principal component analysis (MFPCA) analyzes the entire waveform (i.e., movement pattern), which clinicians observe to direct treatment. This study investigated hip OA indicators, by (1) employing MFPCA to characterize variance across the hip, knee, and ankle angles in healthy and early-to-moderate hip OA participants; and (2) investigating relationships between these waveform features and hip cartilage health. Bilateral hip magnetic resonance images from 72 participants with Kellgren-Lawrence grades ranging from 0 to 3 were used to calculate mean T 1ρ and T 2 relaxation times in the femoral and acetabular cartilage. MFPCA was performed on lower-limb gait biomechanics and used to identify primary modes of variation, which were related to T 1ρ and T 2 relaxation times. Here, a MFPC = mode of variation = waveform feature. In the femoral cartilage, transverse plane MFPCs 3 and 5 and body mass index (BMI) was related to T 1ρ , while MFPC 2 and BMI were related to T 2 relaxation times. In the acetabular cartilage, sagittal plane MFPC 1 and BMI were related to T 1ρ , while BMI was related to T 2 relaxation times. Greater internal rotation was related to increased T 1ρ and T 2 relaxation times in the femoral cartilage, while the greater extension was related to increased T 1ρ relaxation times in the acetabular cartilage. This study established a data-driven framework to assess relationships between multi-joint biomechanics and quantitative assessments of cartilage health and identified waveform features that could be evaluated in future hip OA intervention studies.

ObjectiveTo evaluate the association of fatty infiltration of the quadriceps and vastus medialis (VM) with an increase in knee cartilage, meniscus, or bone marrow lesions, using magnetic resonance imaging (MRI) in knee osteoarthritis (OA) over 3 years.MethodsParticipants (n = 69) with and without radiographic knee OA underwent MRI at baseline and 3 years later. Chemical shift-based water/fat MRI was used to quantify the intramuscular fat fraction and the lean anatomical cross-sectional area (ACSA) for the VM and entire quadriceps muscles. MRI images of the knee were analyzed using the semiquantitative modified whole-organ MRI score (mWORMS) grading to assess change in lesions in the articular cartilage, meniscus, and bone marrow. Logistic regression was used to assess whether baseline quadriceps and VM fat fraction and lean ACSA were associated with an increase in mWORMS scores. Odds ratios (ORs) were adjusted for age, sex, and body mass index.ResultsOverall, of the 69 subjects, 43 (62%) had an increase in cartilage lesions (26 of 43), meniscus lesions (19 of 43), or bone marrow lesions (22 of 43) scores. The quadriceps (OR 2.13 [95% confidence interval (95% CI) 1.09-4.15]) and VM (OR 2.05 [95% CI 1.25-3.36]) fat fraction were both associated with an increase in cartilage, meniscus, or bone marrow lesion scores over 3 years. The association of quadriceps or VM lean ACSA with the outcomes was not significant.ConclusionThese longitudinal findings using quantitative MRI methods for assessment of muscle adiposity highlight the role of quadriceps adiposity, specifically in the VM, in knee OA progression. However, studies in larger cohorts are needed to confirm these findings.

BackgroundAdipose tissue has recently gained interest as an independent imaging biomarker for osteoarthritis.PurposeTo explore 1) cross-sectional associations between local subcutaneous fat (SCF) thickness at the knee and the extent of degenerative changes in overweight and obese individuals and 2) associations between local fat distribution and progression of osteoarthritis over 4 years.Study typeRetrospective cohort study.Population338 obese and overweight participants from the Osteoarthritis Initiative cohort without radiographic evidence of osteoarthritis.Field strength3T: 3D-FLASH-WE; 3D-DESS-WE; T1w-SE; MSME.AssessmentBaseline SCF thickness was measured in standardized locations medial, lateral and anterior to the knee and the average joint-adjacent SCF (ajSCF) was calculated. Right thigh SCF cross-sectional area was assessed. Quantitative cartilage T2 relaxation times and semi-quantitative whole organ MRI scores (WORMS) were obtained at baseline and 4-year follow-up. WORMSsum was calculated as sum of cartilage, bone marrow edema, subchondral cyst, and meniscal scores.Statistical testsAssociations of SCF measures with baseline, and 4-year change in T2 and WORMS were analyzed using regression models. SCF measurements were standardized using the equation ValueParticipant-MeanCohortStandard deviation . Analyses were adjusted for age, sex, physical activity, and BMI.ResultsCross-sectionally, significant associations between lateral SCF, lateral compartment WORMS and T2 were found ( ΔWORMSsum1SDchange in lateralSCF , [95% CI]: 0.53, [0.12-0.95], P