Laser-irradiating infrared attenuated total reflection spectroscopy of articular cartilage: Potential and challenges for diagnosing osteoarthritis.

Objective: A prototype infrared attenuated total reflection (IR-ATR) laser spectroscopic system designed for in vivo classification of human cartilage tissue according to its histological health status during arthroscopic surgery is presented. Prior to real-world in vivo applications, this so-called osteoarthritis (OA) scanner has been tested at in vitro conditions revealing the challenges associated with complex sample matrices and the accordingly obtained sparse spectral datasets. Methods: In vitro studies on human knee cartilage samples at different contact pressures (i.e., 0.2-0.5 â€‹MPa) allowed recording cartilage degeneration characteristic IR signatures comparable to in vivo conditions with high temporal resolution. Afterwards, the cartilage samples were assessed based on the clinically acknowledged osteoarthritis cartilage histopathology assessment (OARSI) system and correlated with the obtained sparse IR data. Results: Amide and carbohydrate signal behavior was observed to be almost identical between the obtained sparse IR data and previously measured FTIR data used for sparse partial least squares discriminant analysis (SPLSDA) to identify the spectral regions relevant to cartilage condition. Contact pressures between 0.3 and 0.4 â€‹MPa seem to provide the best sparse IR spectra for cylindrical (d â€‹= â€‹3 â€‹mm) probe tips. Conclusion: Laser-irradiating IR-ATR spectroscopy is a promising analytical technique for future arthroscopic applications to differentiate healthy and osteoarthritic cartilage tissue. However, this study also revealed that the flexible connection between the laser-based analyzer and the arthroscopic ATR-probe via IR-transparent fiberoptic cables may affect the robustness of the obtained IR data and requires further improvements.

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.

Infrared spectroscopy is suitable for objective assessment of articular cartilage health.

Objective: To evaluate the feasibility of Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy to detect cartilage degradation due to osteoarthritis and to validate the methodology with osteochondral human cartilage samples for future development towards clinical use. Design: Cylindrical (d â€‹= â€‹4 â€‹mm) osteochondral samples (n â€‹= â€‹349) were prepared from nine human cadavers and measured with FTIR-ATR spectroscopy. Afterwards, the samples were assessed with Osteoarthritis Research Society International (OARSI) osteoarthritis cartilage histopathology assessment system and divided into two groups: 1) healthy (OARSI 0-2) and 2) osteoarthritic (OARSI 2.5-6). The classification was done with partial least squares discriminant analysis model utilizing cross-model validation. Receiver operating characteristics curve analysis was performed and the area under curve (AUC) was calculated. Results: For all samples combined, classification accuracy was 73% with AUC of 0.79. Femoral samples had accuracy of 74% and AUC of 0.77, while tibial samples had accuracy of 66%, and AUC of 0.74. Patellar samples had accuracy of 84% and AUC of 0.91. Conclusions: The results indicate that FTIR-ATR spectroscopy can differentiate between healthy and osteoarthritic femoral, tibial and patellar human tissue. If combined with a fiber optic probe, FTIR-ATR spectroscopy could provide additional objective intraoperative information during arthroscopic surgeries, which could improve clinical outcomes.

The CMS19 disease model specifies a pivotal role for collagen XIII in bone homeostasis.

Mutations in the COL13A1 gene result in congenital myasthenic syndrome type 19 (CMS19), a disease of neuromuscular synapses and including various skeletal manifestations, particularly facial dysmorphisms. The phenotypic consequences in Col13a1 null mice (Col13a1-/-) recapitulate the muscle findings of the CMS19 patients. Collagen XIII (ColXIII) is exists as two forms, a transmembrane protein and a soluble molecule. While the Col13a1-/- mice have poorly formed neuromuscular junctions, the prevention of shedding of the ColXIII ectodomain in the Col13a1tm/tm mice results in acetylcholine receptor clusters of increased size and complexity. In view of the bone abnormalities in CMS19, we here studied the tubular and calvarial bone morphology of the Col13a1-/- mice. We discovered several craniofacial malformations, albeit less pronounced ones than in the human disease, and a reduction of cortical bone mass in aged mice. In the Col13a1tm/tm mice, where ColXIII is synthesized but the ectodomain shedding is prevented due to a mutation in a protease recognition sequence, the cortical bone mass decreased as well with age and the cephalometric analyses revealed significant craniofacial abnormalities but no clear phenotypical pattern. To conclude, our data indicates an intrinsic role for ColXIII, particularly the soluble form, in the upkeep of bone with aging and suggests the possibility of previously undiscovered bone pathologies in patients with CMS19.

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.

Design and development of poly-L/D-lactide copolymer and barium titanate nanoparticle 3D composite scaffolds using breath figure method for tissue engineering applications.

In tissue engineering, the scaffold topography influences the adhesion, proliferation, and function of cells. Specifically, the interconnected porosity is crucial for cell migration and nutrient delivery in 3D scaffolds. The objective of this study was to develop a 3D porous composite scaffold for musculoskeletal tissue engineering applications by incorporating barium titanate nanoparticles (BTNPs) into a poly-L/D-lactide copolymer (PLDLA) scaffold using the breath figure method. The porous scaffold fabrication utilised 96/04 PLDLA, dioleoyl phosphatidylethanolamine (DOPE), and different types of BTNPs, including uncoated BTNPs, Al2O3-coated BTNPs, and SiO2-coated BTNPs. The BTNPs were incorporated into the polymer scaffold, which was subsequently analysed using field emission scanning electron microscopy (FE-SEM). The biocompatibility of each scaffold was tested using ovine bone marrow stromal stem cells. The cell morphology, viability, and proliferation were evaluated using FE-SEM, LIVE/DEAD staining, and Prestoblue assay. Porous 3D composite scaffolds were successfully produced, and it was observed that the incorporation of uncoated BTNPs increased the average pore size from 1.6 µm (PLDLA) to 16.2 µm (PLDLA/BTNP). The increased pore size in the PLDLA/BTNP scaffolds provided a suitable porosity for the cells to migrate inside the scaffold, while in the pure PLDLA scaffolds with their much smaller pore size, cells elongated on the surface. To conclude, the breath figure method was successfully used to develop a PLDLA/BTNP scaffold. The use of uncoated BTNPs resulted in a composite scaffold with an optimal pore size while maintaining the honeycomb-like structure. The composite scaffolds were biocompatible and yielded promising structures for future tissue engineering applications.

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.

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).

Detection of experimental cartilage damage with acoustic emissions technique: An in vitro equine study.

BACKGROUND: In horses, osteoarthritis (OA) mostly affects metacarpophalangeal and metatarsophalangeal (fetlock) joints. The current modalities used for diagnosis of equine limb disorders lack ability to detect early OA. Here, we propose a new alternative approach to assess experimental cartilage damage in fetlock joint using Acoustic Emissions (AE). OBJECTIVES: To evaluate the potential of AE technique in diagnosing OA and see how AE signals changes with increasing severity of OA. STUDY DESIGN: An in vitro experimental study. METHODS: A total of 16 distal limbs (8 forelimbs and 8 hindlimbs) from six Finn horses were collected from an abattoir and fitted in a custom-made frame allowing fetlock joint bending. Eight fetlock joints were opened, and cartilage surface was progressively damaged mechanically three times using sandpaper to mimic mild, moderate and severe OA. The remaining eight fetlock joints were opened and closed without any mechanical procedure, serving as controls. Before cartilage alteration, synovial fluid was aspirated, mixed with phosphate-buffered saline solution, and then reinjected before suturing for constant joint lubrication. For each simulated condition of OA severity, a force was applied to the frame and then released to mimic joint flexion and extension. AE signals were acquired using air microphones. RESULTS: A strong association was found between the joint condition and the power of AE signals analysed in 1.5-6 kHz range. The signal from both forelimb and hindlimb joints followed a similar pattern for increased cartilage damage. There were statistically significant differences between each joint condition progressively (generalised linear mixed model, P<0.001) in limbs with in vitro cartilage damage of varying severity while the control limbs did not show any changes. MAIN LIMITATIONS: Small sample size using in vitro, mechanically induced cartilage damage. CONCLUSION: The AE technique presented here could differentiate the severity of fetlock joint cartilage damage. The consistent results for each simulated condition suggest there is potential for this method in the diagnosis of OA.

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography.

The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r| > 0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r = 0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R2 change = 0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r| > 0.4). In conclusion, we demonstrated that GLCM- and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.

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.

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.

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.

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.

Composition, structure and tensile biomechanical properties of equine articular cartilage during growth and maturation.

Articular cartilage undergoes structural and biochemical changes during maturation, but the knowledge on how these changes relate to articular cartilage function at different stages of maturation is lacking. Equine articular cartilage samples of four different maturation levels (newborn, 5-month-old, 11-month-old and adult) were collected (N = 25). Biomechanical tensile testing, Fourier transform infrared microspectroscopy (FTIR-MS) and polarized light microscopy were used to study the tensile, biochemical and structural properties of articular cartilage, respectively. The tensile modulus was highest and the breaking energy lowest in the newborn group. The collagen and the proteoglycan contents increased with age. The collagen orientation developed with age into an arcade-like orientation. The collagen content, proteoglycan content, and collagen orientation were important predictors of the tensile modulus (p < 0.05 in multivariable regression) and correlated significantly also with the breaking energy (p < 0.05 in multivariable regression). Partial least squares regression analysis of FTIR-MS data provided accurate predictions for the tensile modulus (r = 0.79) and the breaking energy (r = 0.65). To conclude, the composition and structure of equine articular cartilage undergoes changes with depth that alter functional properties during maturation, with the typical properties of mature tissue reached at the age of 5-11 months.