3D analysis and grading of calcifications from ex vivo human meniscus.

OBJECTIVE: Meniscal calcifications are associated with the pathogenesis of knee osteoarthritis (OA). We propose a micro-computed tomography (µCT) based 3D analysis of meniscal calcifications ex vivo, including a new grading system. METHOD: Human medial and lateral menisci were obtained from 10 patients having total knee replacement for medial compartment OA and 10 deceased donors without knee OA (healthy references). The samples were fixed; one subsection was imaged with µCT, and the adjacent tissue was processed for histological evaluation. Calcifications were examined from the reconstructed 3D µCT images, and a new grading system was developed. To validate the grading system, meniscal calcification volumes (CVM) were quantitatively analyzed and compared between the calcification grades. Furthermore, we estimated the relationship between histopathological degeneration and the calcification severity. RESULTS: 3D µCT images depict calcifications in every sample, including diminutive calcifications that are not visible in histology. In the new grading system, starting from grade 2, each grade results in a CVM that is 20.3 times higher (95% CI 13.3-30.5) than in the previous grade. However, there was no apparent difference in CVM between grades 1 and 2. The calcification grades appear to increase with the increasing histopathological degeneration, although histopathological degeneration is also observed with small calcification grades. CONCLUSIONS: 3D µCT grading of meniscal calcifications is feasible. Interestingly, it seems that there are two patterns of degeneration in the menisci of our sample set: 1) with diminutive calcifications (calcification grades 1-2), and 2) with large to widespread calcifications (calcification grades 3-5).

The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images.

OBJECTIVES: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. DESIGN: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). RESULTS: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57-0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52-0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. CONCLUSION: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.

Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT.

We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images. INTRODUCTION: In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT). METHODS: The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher. The study data was split into 55% training, 30% validation, and 15% test sets. A pretrained ResNet18 was optimized for a classification task of rHOA vs. no-rHOA. Five models were trained using (1) X-rays, (2) downsampled X-rays, (3) combination of CT-AP and X-ray images, (4) combination of CT-AP and downsampled X-ray images, and (5) CT-AP images. RESULTS: Amongst the five models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images. Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve (ROC AUC) of 0.93 [0.75-0.99]. Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89 [0.67-0.97]. CONCLUSION: CT-based summation images that resemble radiographs can be used to detect rHOA. In addition, in the absence of large training data, a reliable DL model can be optimized by combining CT-AP and X-ray images.

Automated detection of patellofemoral osteoarthritis from knee lateral view radiographs using deep learning: data from the Multicenter Osteoarthritis Study (MOST).

OBJECTIVE: To assess the ability of imaging-based deep learning to detect radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. DESIGN: Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 18,436 knees). Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA. Patellar ROI was detected using deep-learning-based object detection method. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of classification models was assessed using the area under the receiver operating characteristic curve (ROC AUC) and the average precision (AP) obtained from the Precision-Recall (PR) curve in the stratified 5-fold cross validation setting. RESULTS: Of the 18,436 knees, 3,425 (19%) had PFOA. AUC and AP for the reference model including age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade to detect PFOA were 0.806 and 0.478, respectively. The CNN model that used only image data significantly improved the classifier performance (ROC AUC = 0.958, AP = 0.862). CONCLUSION: We present the first machine learning based automatic PFOA detection method. Furthermore, our deep learning based model trained on patella region from knee lateral view radiographs performs better at detecting PFOA than models based on patient characteristics and clinical assessments.

Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study.

OBJECTIVE: To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-occurrence matrix (GLCM) and LBP texture data. Classification algorithms were used to reduce the dimensionality of texture data into a likelihood of subject belonging to the reference class. METHOD: T2 relaxation time mapping with multi-slice multi-echo spin echo sequence was performed for eighty symptomatic OA patients and 63 asymptomatic controls on a 3T clinical MRI scanner. Relaxation time maps were subjected to GLCM and LBP texture analysis, and classification algorithms were deployed with an in-house developed software. Implemented algorithms were K nearest neighbors, support vector machine, and neural network classifier. RESULTS: LBP and GLCM discerned OA patients from controls with a significant difference in all studied regions. Classification models comprising GLCM and LBP showed high accuracy in classing OA patients and controls. The best performance was obtained with a multilayer perceptron type classifier with an overall accuracy of 90.2 %. CONCLUSION: LBP texture analysis complements prior results with GLCM, and together LBP and GLCM serve as significant input data for classification algorithms trained for OA assessment. Presented algorithms are adaptable to versatile OA evaluations also for future gradational or predictive approaches.

Quantitative three-dimensional collagen orientation analysis of human meniscus posterior horn in health and osteoarthritis using micro-computed tomography.

OBJECTIVE: Knee osteoarthritis (OA) is associated with meniscal degeneration that may involve disorganization of the meniscal collagen fiber network. Our aims were to quantitatively analyze the microstructural organization of human meniscus samples in 3D using micro-computed tomography (µCT), and to compare the local microstructural organization between OA and donor samples. METHOD: We collected posterior horns of both medial and lateral human menisci from 10 end-stage medial compartment knee OA patients undergoing total knee replacement (medial & lateral OA) and 10 deceased donors without knee OA (medial & lateral donor). Posterior horns were dissected and fixed in formalin, dehydrated in ascending ethanol concentrations, treated with hexamethyldisilazane (HMDS), and imaged with µCT. We performed local orientation analysis of collagenous microstructure in 3D by calculating structure tensors from greyscale gradients within selected integration window to determine the polar angle for each voxel. RESULTS: In donor samples, meniscus bundles were aligned circumferentially around the inner border of meniscus. In medial OA menisci, the organized structure of collagen network was lost, and main orientation was shifted away from the circumferential alignment. Quantitatively, medial OA menisci had the lowest mean orientation angle compared to all groups, -24° (95%CI -31 to -18) vs medial donor and -25° (95%CI -34 to -15) vs lateral OA. CONCLUSIONS: HMDS-based µCT imaging enabled quantitative analysis of meniscal collagen fiber bundles and their orientations in 3D. In human medial OA menisci, the collagen disorganization was profound with overall lower orientation angles, suggesting collagenous microstructure disorganization as an important part of meniscus degradation.

Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis.

OBJECTIVE: The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). DESIGN: Bilateral posterior-anterior knee radiographs were analyzed from the baseline of Osteoarthritis Initiative (OAI) (9012 knee radiographs) and Multicenter Osteoarthritis Study (MOST) (3,644 knee radiographs) datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. Subsequently, we built logistic regression models to identify and compare the performances of several texture descriptors and each ROI placement method using 5-fold cross validation. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset. We used area under the receiver operating characteristic curve (ROC AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. RESULTS: We found that the adaptive ROI improves the classification performance (OA vs non-OA) over the commonly-used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, Local Binary Pattern (LBP) yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. CONCLUSION: Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.

Development of osteoarthritis in patients with degenerative meniscal tears treated with exercise therapy or surgery: a randomized controlled trial.

OBJECTIVE: To evaluate progression of individual radiographic features 5 years following exercise therapy or arthroscopic partial meniscectomy as treatment for degenerative meniscal tear. DESIGN: Randomized controlled trial including 140 adults, aged 35-60 years, with a magnetic resonance image verified degenerative meniscal tear, and 96% without definite radiographic knee osteoarthritis. Participants were randomized to either 12-weeks of supervised exercise therapy or arthroscopic partial meniscectomy. The primary outcome was between-group difference in progression of tibiofemoral joint space narrowing and marginal osteophytes at 5 years, assessed semi-quantitatively by the OARSI atlas. Secondary outcomes included incidence of radiographic knee osteoarthritis and symptomatic knee osteoarthritis, medial tibiofemoral fixed joint space width (quantitatively assessed), and patient-reported outcome measures. Statistical analyses were performed using a full analysis set. Per protocol and as treated analysis were also performed. RESULTS: The risk ratios (95% CI) for progression of semi-quantitatively assessed joint space narrowing and medial and lateral osteophytes for the surgery group were 0.89 (0.55-1.44), 1.15 (0.79-1.68) and 0.77 (0.42-1.42), respectively, compared to the exercise therapy group. In secondary outcomes (full-set analysis) no statistically significant between-group differences were found. CONCLUSION: The study was inconclusive with respect to potential differences in progression of individual radiographic features after surgical and non-surgical treatment for degenerative meniscal tear. Further, we found no strong evidence in support of differences in development of incident radiographic knee osteoarthritis or patient-reported outcomes between exercise therapy and arthroscopic partial meniscectomy. TRIAL REGISTRATION: www.clinicaltrials.gov (NCT01002794).

Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography.

OBJECTIVE: To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEµCT). DESIGN: A total of 79 osteochondral cores from 24 total knee arthroplasty patients and two asymptomatic donors were imaged using CEµCT with phosphotungstic acid -staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depth-wise and subjected to dimensionally reduced Local Binary Pattern -textural feature analysis. Regularized linear and logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEµCT grades (diameter = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (4 mm samples). The performance was primarily assessed using Mean Squared Error (MSE) and Average Precision (AP, confidence intervals are given in square brackets). RESULTS: Highest performance on cross-validation was observed for SZ, both on linear regression (MSE = 0.49, 0.69 and 0.71 for SZ, DZ and CZ, respectively) and LR (AP = 0.9 [0.77-0.99], 0.46 [0.28-0.67] and 0.65 [0.41-0.85] for SZ, DZ and CZ, respectively). The test set evaluations yielded increased MSE on all zones. For LR, the performance was also best for the SZ (AP = 0.85 [0.73-0.93], 0.82 [0.70-0.92] and 0.8 [0.67-0.9], for SZ, DZ and CZ, respectively). CONCLUSION: We present the first ML-based automatic 3D histopathological osteoarthritis (OA) grading method which also adequately perform on grading unseen data, especially in SZ. After further development, the method could potentially be applied by OA researchers since the grading software and all source codes are publicly available.

Effect of centrifugal force on the development of articular neocartilage with bovine primary chondrocytes.

A lot has been invested into understanding how to assemble cartilage tissue in vitro and various designs have been developed to manufacture cartilage tissue with native-like biological properties. So far, no satisfactory design has been presented. Bovine primary chondrocytes are used to self-assemble scaffold-free constructs to investigate whether mechanical loading by centrifugal force would be useful in manufacturing cartilage tissue in vitro. Six million chondrocytes were laid on top of defatted bone disks placed inside an agarose well in 50-ml culture tubes. The constructs were centrifuged once or three times per day for 15 min at a centrifugal force of 771×g for up to 4 weeks. Control samples were cultured under the same conditions without exposure to centrifugation. The samples were analysed by (immuno)histochemistry, Fourier transform infrared imaging, micro-computed tomography, biochemical and gene expression analyses. Biomechanical testing was also performed. The centrifuged tissues had a more even surface covering a larger area of the bone disk. Fourier transform infrared imaging analysis indicated a higher concentration of collagen in the top and bottom edges in some of the centrifuged samples. Glycosaminoglycan contents increased along the culture, while collagen content remained at a rather constant level. Aggrecan and procollagen α1(II) gene expression levels had no significant differences, while procollagen α2(I) levels were increased significantly. Biomechanical analyses did not reveal remarkable changes. The centrifugation regimes lead to more uniform tissue constructs, whereas improved biological properties of the native tissue could not be obtained by centrifugation.

Osteoarthritis year in review 2018: imaging.

PURPOSE: To provide a narrative review of the most relevant original research published in 2017/2018 on osteoarthritis imaging. METHODS: The PubMed database was used to recover all relevant articles pertaining to osteoarthritis and medical imaging published between 1 April 2017 and 31 March 2018. Review articles, case studies and in vitro or animal studies were excluded. The original publications were subjectively sorted based on relevance, novelty and impact. RESULTS AND CONCLUSIONS: The publication search yielded 1,155 references. In the assessed publications, the most common imaging modalities were radiography (N = 708) and magnetic resonance imaging (MRI) (355), followed by computed tomography (CT) (220), ultrasound (85) and nuclear medicine (17). An overview of the most important publications to the osteoarthritis (OA) research community is presented in this narrative review. Imaging studies play an increasingly important role in OA research, and have helped us to understand better the pathophysiology of OA. Radiography and MRI continue to be the most applied imaging modalities, while quantitative MRI methods and texture analysis are becoming more popular. The value of ultrasound in OA research has been demonstrated. Several multi-modality predictive models have been developed. Deep learning has potential for more automatic and standardized analyses in future OA imaging research.

Three-dimensional microstructure of human meniscus posterior horn in health and osteoarthritis.

OBJECTIVE: To develop and perform ex vivo 3D imaging of meniscus posterior horn microstructure using micro-computed tomography (µCT), and to compare specimens from healthy references against end-stage osteoarthritis (OA) using conventional section-based histology and qualitative µCT. DESIGN: We retrieved human medial and lateral menisci from 10 deceased donors without knee OA (healthy references) and medial and lateral menisci from 10 patients having total knee replacement for medial compartment OA. Meniscal posterior horns were dissected and fixed in formalin. One subsection underwent hexamethyldisilazane (HMDS) treatment and µCT imaging. Pauli's histopathological scoring was performed for 3 other subsections. The differences in histopathological scores were estimated using mixed linear regression, resulting in fixed effects estimates for within-knee comparisons and adjusted for age and body mass index for between-subjects comparisons. RESULTS: 3D visualization with µCT qualitatively revealed similar microstructural changes in the posterior horns as conventional histology. The mean histopathological score was higher for medial menisci from OA knees vs both medial reference menisci (mean difference [95% CI], 3.9 [2.6,5.3]), and lateral menisci from OA knees (3.9 [2.9,5.0]). The scores were similar between lateral menisci from OA knees and lateral reference menisci (0.8 [-0.6,2.2]), and between medial and lateral reference menisci (0.8 [-0.3,1.9]). CONCLUSIONS: HMDS-based µCT protocol allows unique 3D visualization of meniscus microstructures. Posterior horns of medial menisci from medial compartment OA knees had higher histopathological scores than both the lateral posterior horns from the same OA knees and medial reference menisci, suggesting a strong association between meniscus degradation and unicompartmental knee OA.

3D morphometric analysis of calcified cartilage properties using micro-computed tomography.

OBJECTIVE: Our aim is to establish methods for quantifying morphometric properties of calcified cartilage (CC) from micro-computed tomography (µCT). Furthermore, we evaluated the feasibility of these methods in investigating relationships between osteoarthritis (OA), tidemark surface morphology and open subchondral channels (OSCCs). METHOD: Samples (n = 15) used in this study were harvested from human lateral tibial plateau (n = 8). Conventional roughness and parameters assessing local 3-dimensional (3D) surface variations were used to quantify the surface morphology of the CC. Subchondral channel properties (percentage, density, size) were also calculated. As a reference, histological sections were evaluated using Histopathological osteoarthritis grading (OARSI) and thickness of CC and subchondral bone (SCB) was quantified. RESULTS: OARSI grade correlated with a decrease in local 3D variations of the tidemark surface (amount of different surface patterns (rs = -0.600, P = 0.018), entropy of patterns (EP) (rs = -0.648, P = 0.018), homogeneity index (HI) (rs = 0.555, P = 0.032)) and tidemark roughness (TMR) (rs = -0.579, P = 0.024). Amount of different patterns (ADP) and EP associated with channel area fraction (CAF) (rp = 0.876, P < 0.0001; rp = 0.665, P = 0.007, respectively) and channel density (CD) (rp = 0.680, P = 0.011; rp = 0.582, P = 0.023, respectively). TMR was associated with CAF (rp = 0.926, P < 0.0001) and average channel size (rp = 0.574, P = 0.025). CC topography differed statistically significantly in early OA vs healthy samples. CONCLUSION: We introduced a µ-CT image method to quantify 3D CC topography and perforations through CC. CC topography was associated with OARSI grade and OSCC properties; this suggests that the established methods can detect topographical changes in tidemark and CC perforations associated with OA.

Clinimetrics of ultrasound pathologies in osteoarthritis: systematic literature review and meta-analysis.

OBJECTIVE: The aims of this study were to systematically review clinimetrics of commonly assessed ultrasound pathologies in knee, hip and hand osteoarthritis (OA), and to conduct a meta-analysis for each clinimetric. METHODS: Medline, Embase, and Cochrane Library databases were searched from their inceptions to September 2016. According to the Outcome Measures in Rheumatology (OMERACT) Instrument Selection Algorithm, data extraction focused on ultrasound technical features and performance metrics. Methodological quality was assessed with modified 19-item Downs and Black score and 11-item Quality Appraisal of Diagnostic Reliability (QAREL) score. Separate meta-analyses were performed for clinimetrics: (1) inter-rater/intra-rater reliability; (2) construct validity; (3) criteria validity; and (4) internal/external responsiveness. Statistical Package for the Social Sciences (SPSS), Excel and Comprehensive Meta-analysis were used. RESULT: Our search identified 1126 records; of these, 100 were eligible, including a total of 8542 patients and 32,373 joints. The average Downs and Black score was 13.01, and average QAREL was 5.93. The stratified meta-analysis was performed only for knee OA, which demonstrated moderate to substantial reliability [minimum kappa > 0.44(0.15,0.74), minimum intraclass correlation coefficient (ICC) > 0.82(0.73-0.89)], weak construct validity against pain (r = 0.12 to 0.27), function (r = 0.15 to 0.23), and blood biomarkers (r = 0.01 to 0.21), but weak to strong correlation with plain radiography (r = 0.13 to 0.60), strong association with Magnetic Resonance Imaging (MRI) [minimum r = 0.60(0.52,0.67)] and strong discrimination against symptomatic patients (OR = 3.08 to 7.46). There was strong criterion validity against cartilage histology [r = 0.66(-0.05,0.93)], and small to moderate internal [standardized mean difference(SMD) = 0.20 to 0.58] and external (r = 0.35 to 0.43) responsiveness to interventions. CONCLUSION: Ultrasound demonstrated strong criterion validity with cartilage histology, poor to strong correlation with patient findings and MRI, moderate reliability, and low responsiveness to interventions. PROSPERO REGISTRATION NO: CRD42016039954.

Assessment of meniscus with adiabatic T1ρ and T2ρ relaxation time in asymptomatic subjects and patients with mild osteoarthritis: a feasibility study.

OBJECTIVE: To investigate the ability of magnetic resonance imaging (MRI) adiabatic relaxation times in the rotating frame (adiabatic T1ρ and T2ρ) to detect structural alterations in meniscus tissue of mild OA patients and asymptomatic volunteers. METHOD: MR images of 24 subjects (age range: 50-67 years, 12 male), including 12 patients with mild osteoarthritis (OA) (Kellgren-Lawrence (KL) = 1, 2) and 12 asymptomatic volunteers, were acquired using a 3 T clinical MRI system. Morphological assessment was performed using semiquantitative MRI OA Knee Score (MOAKS). Adiabatic T1ρ and T2ρ (AdT1ρ, AdT2ρ) relaxation time maps were calculated in regions of interest (ROIs) containing medial and lateral horns of menisci. The median relaxation time values of the ROIs were compared between subjects classified based on radiographic findings and MOAKS evaluations. RESULTS: MOAKS assessment of patients and volunteers indicated the presence of meniscal and cartilage lesions in both groups. For the combined cohort group, prolonged AdT1ρ was observed in the posterior horn of the medial meniscus (PHMED) in subjects with MOAKS meniscal tear (P < 0.05). AdT2ρ was statistically significantly longer in PHMED of subjects with MOAKS full-thickness cartilage loss (P < 0.05). After adjusting for multiple comparisons, differences in medians of observed AdT1ρ and AdT2ρ values between mild OA patients and asymptomatic volunteers did not reach statistical significance. CONCLUSION: AdT1ρ and AdT2ρ measurements have the potential to identify changes in structural composition of meniscus tissue associated with meniscal tear and cartilage loss in a cohort group of mild OA patients and asymptomatic volunteers.

Electromechanical properties of human osteoarthritic and asymptomatic articular cartilage are sensitive and early detectors of degeneration.

OBJECTIVE: To evaluate cross-correlations of ex vivo electromechanical properties with cartilage and subchondral bone plate thickness, as well as their sensitivity and specificity regarding early cartilage degeneration in human tibial plateau. METHOD: Six pairs of tibial plateaus were assessed ex vivo using an electromechanical probe (Arthro-BST) which measures a quantitative parameter (QP) reflecting articular cartilage compression-induced streaming potentials. Cartilage thickness was then measured with an automated thickness mapping technique using Mach-1 multiaxial mechanical tester. Subsequently, a visual assessment was performed by an experienced orthopedic surgeon using the International Cartilage Repair Society (ICRS) grading system. Each tibial plateau was finally evaluated with µCT scanner to determine the subchondral-bone plate thickness over the entire surface. RESULTS: Cross-correlations between assessments decreased with increasing degeneration level. Moreover, electromechanical QP and subchondral-bone plate thickness increased strongly with ICRS grade (ρ = 0.86 and ρ = 0.54 respectively), while cartilage thickness slightly increased (ρ = 0.27). Sensitivity and specificity analysis revealed that the electromechanical QP is the most performant to distinguish between different early degeneration stages, followed by subchondral-bone plate thickness and then cartilage thickness. Lastly, effect sizes of cartilage and subchondral-bone properties were established to evaluate whether cartilage or bone showed the most noticeable changes between normal (ICRS 0) and each early degenerative stage. Thus, the effect sizes of cartilage electromechanical QP were almost twice those of the subchondral-bone plate thickness, indicating greater sensitivity of electromechanical measurements to detect early osteoarthritis. CONCLUSION: The potential of electromechanical properties for the diagnosis of early human cartilage degeneration was highlighted and supported by cartilage thickness and µCT assessments.

In vitro method for 3D morphometry of human articular cartilage chondrons based on micro-computed tomography.

OBJECTIVE: The aims of this study were: to 1) develop a novel sample processing protocol to visualize human articular cartilage (AC) chondrons using micro-computed tomography (µCT), 2) develop and validate an algorithm to quantify the chondron morphology in 3D, and 3) compare the differences in chondron morphology between intact and osteoarthritic AC. METHOD: The developed protocol is based on the dehydration of samples with hexamethyldisilazane (HMDS), followed by imaging with a desktop µCT. Chondron density and depth, as well as volume and sphericity, were calculated in 3D with a custom-made and validated algorithm employing semi-automatic chondron selection and segmentation. The quantitative parameters were analyzed at three AC depth zones (zone 1: 0-10%; zone 2: 10-40%; zone 3: 40-100%) and grouped by the OARSI histological grades (OARSI grades 0-1.0, n = 6; OARSI grades 3.0-3.5, n = 6). RESULTS: After semi-automatic chondron selection and segmentation, 1510 chondrons were approved for 3D morphometric analyses. The chondrons especially in the deeper tissue (zones 2 and 3) were significantly larger (P < 0.001) and less spherical (P < 0.001), respectively, in the OARSI grade 3-3.5 group compared to the OARSI grade 0-1.0 group. No statistically significant difference in chondron density between the OARSI grade groups was observed at different depths. CONCLUSION: We have developed a novel sample processing protocol for chondron imaging in 3D, as well as a high-throughput algorithm to semi-automatically quantify chondron/chondrocyte 3D morphology in AC. Our results also suggest that 3D chondron morphology is affected by the progression of osteoarthritis (OA).

Association between radiography-based subchondral bone structure and MRI-based cartilage composition in postmenopausal women with mild osteoarthritis.

OBJECTIVE: Our aim was to investigate the relation between radiograph-based subchondral bone structure and cartilage composition assessed with delayed gadolinium enhanced magnetic resonance imaging of cartilage (dGEMRIC) and T2 relaxation time. DESIGN: Ninety-three postmenopausal women (Kellgren-Lawrence grade 0: n = 13, 1: n = 26, 2: n = 54) were included. Radiograph-based bone structure was assessed using entropy of the Laplacian-based image (ELap) and local binary patterns (ELBP), homogeneity indices of the local angles (HIAngles,mean, HIAngles,Perp, HIAngles,Paral), and horizontal (FDHor) and vertical fractal dimensions (FDVer). Mean dGEMRIC index and T2 relaxation time of tibial cartilage were calculated to estimate cartilage composition. RESULTS: HIAngles,mean (rs = -0.22) and HIAngles,Paral (rs = -0.24) in medial subchondral bone were related (P < 0.05) to dGEMRIC index of the medial tibial cartilage. ELap (rs = -0.23), FDHor,0.34 mm (r = 0.21) and FDVer,0.68 mm (r = 0.24) in medial subchondral bone were related (P < 0.05) to T2 relaxation time values of the medial tibial cartilage. FDHor at different scales in lateral subchondral bone were related (P < 0.01) to dGEMRIC index (r = 0.29-0.41) and T2 values of lateral tibial cartilage (r = -0.28 to -0.36). FDVer at larger scales were related (P < 0.05) to dGEMRIC index (r = 0.24-0.25) and T2 values of lateral tibial cartilage (r = -0.21). HIAngles,Paral (r = -0.25) and FDVer,0.68 mm (rs = 0.22) in the lateral tibial trabecular bone were related (P < 0.05) to dGEMRIC index of the lateral tibial cartilage. CONCLUSION: Our results support the presumption that several tissues are affected in the early osteoarthritis (OA). Furthermore, they indicate that the detailed analysis of radiographs may serve as a complementary imaging tool for OA studies.

3D histopathological grading of osteochondral tissue using contrast-enhanced micro-computed tomography.

OBJECTIVE: Histopathological grading of osteochondral (OC) tissue is widely used in osteoarthritis (OA) research, and it is relatively common in post-surgery in vitro diagnostics. However, relying on thin tissue section, this approach includes a number of limitations, such as: (1) destructiveness, (2) sample processing artefacts, (3) 2D section does not represent spatial 3D structure and composition of the tissue, and (4) the final outcome is subjective. To overcome these limitations, we recently developed a contrast-enhanced µCT (CEµCT) imaging technique to visualize the collagenous extracellular matrix (ECM) of articular cartilage (AC). In the present study, we demonstrate that histopathological scoring of OC tissue from CEµCT is feasible. Moreover, we establish a new, semi-quantitative OA µCT grading system for OC tissue. RESULTS: Pathological features were clearly visualized in AC and subchondral bone (SB) with µCT and verified with histology, as demonstrated with image atlases. Comparison of histopathological grades (OARSI or severity (0-3)) across the characterization approaches, CEµCT and histology, excellent (0.92, 95% CI = [0.84, 0.96], n = 30) or fair (0.50, 95% CI = [0.16, 0.74], n = 27) intra-class correlations (ICC), respectively. A new µCT grading system was successfully established which achieved an excellent cross-method (µCT vs histology) reader-to-reader intra-class correlation (0.78, 95% CI = [0.58, 0.89], n = 27). CONCLUSIONS: We demonstrated that histopathological information relevant to OA can reliably be obtained from CEµCT images. This new grading system could be used as a reference for 3D imaging and analysis techniques intended for volumetric evaluation of OA pathology in research and clinical applications.

Magnetic resonance imaging (MRI)-defined cartilage degeneration and joint pain are associated with poor physical function in knee osteoarthritis - the Oulu Knee Osteoarthritis study.

OBJECTIVE: The main aim was to investigate the associations between Magnetic Resonance Imaging (MRI)-defined structural pathologies of the knee and physical function. DESIGN: A cohort study with frequency matching on age and sex with eighty symptomatic subjects with knee pain and suspicion or diagnosis of knee osteoarthritis (OA) and 57 asymptomatic subjects was conducted. The subjects underwent knee MRI, and the severity of structural changes was graded by MRI Osteoarthritis Knee Score (MOAKS) in separate knee locations. WOMAC function subscores were recorded and physical function tests (20-m and 5-min walk, stair ascending and descending, timed up & go and repeated sit-to-stand tests) performed. The association between MRI-defined structural pathologies and physical function tests and WOMAC function subscores were evaluated by linear regression analysis with adjustment for demographic factors, other MRI-features and pain with using effect size (ES) as a measure of the magnitude of an association. RESULTS: Cartilage degeneration showed significant association with poor physical performance in TUG-, stair ascending and descending-, 20-m- and 5-min walk-tests (ESs in the subjects with cartilage degeneration anywhere between 0.134 [95%CI 0.037-0.238] and 0.224 [0.013-0.335]) and with increased WOMAC function subscore (ES in the subjects with cartilage degeneration anywhere 0.088 [0.012-0.103]). Also, lateral meniscus maceration and extrusion were associated with poor performance in stair ascending test (ESs 0.067 [0.008-0.163] and 0.077 [0.012-0.177]). CONCLUSIONS: After adjustments cartilage degeneration was associated with both decreased self-reported physical function and poor performance in the physical function tests. Furthermore, subjects with lateral meniscus maceration and extrusions showed significantly worse performance in stair ascending tests.