Evaluation of cartilage defects with near-infrared spectroscopy (NIR): an ex vivo study

Applying virtual sample generation and ensemble modeling for improving the spectral diagnosis of cancer

Cancer diagnosis plays a key role in facilitating treatment and improving survival rates of patients. The combination of near-infrared (NIR) spectroscopy with data-driven algorithms offers a rapid and cost-effective approach for such a task. Due to the limitations of objective cases, the number of tumor samples is usually smaller, and the resulting dataset exhibit the issues of class imbalance, which has a more serious impact on the performance of diagnostic models. To deal with class imbalance and improve the sensitivity, this work investigates the feasibility of NIR spectroscopy combined with virtual sample generation (VSG) as well as ensemble strategy for developing diagnostic models. Based on preliminary experiment, several learning algorithms such as discriminant analysis (DA) and partial least square-discriminant analysis (PLS-DA) are screened out as algorithms for constructing prediction models. Three algorithms of VSG including synthetic minority oversampling technique (SMOTE), Borderline-SMOTE and adaptive synthetic sampling (ADASYN) are used for experiment. A fixed sample subset composed of 27 cancer samples and 54 normal samples are hold out as the test set. Three training sets containing 5, 10, 25 minority class samples and 54 majority class samples are used for model development. The experimental result indicates that overall, with PLS-DA algorithm, all VSG approaches can significantly improve the sensitivity of cancer diagnosis for all cases of training sets with different minority samples, but ADASYN performs the best. It reveals that the integration of NIR, PLS-DA, and ADASYN is a promising tool package for developing diagnosis methods.

NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis

Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identification of tissue cancerization. In this study, NIR spectroscopic detection equipped with the lab-made NIR probe was performed to in situ explore the change of molecular compositions in breast cancerization, where the diffused NIR spectra were efficiently collected at different locations of cancerous and paracancerous areas. The breast cancerous-paracancerous discriminant model was established based on one-dimensional convolutional neural network (1D-CNN). By optimizing the structure of the neural network, the high classification accuracy (94.67%), recall/sensitivity (95.33%), specificity (94.00%), precision (94.08%) and F1 score (0.9470) were achieved, showing the better discrimination ability and reliability than the K-Nearest Neighbor (KNN, 88.34%, 98.21%, 76.11%, 83.59%, 0.9031) and Fisher Discriminant Analysis (FDA, 90.00%, 96.43%, 81.82%, 87.10%, 0.9153) methods. The experimental results indicate that the application of 1D-CNN can discriminate the cancerous and paracancerous breast tissues, and provide an intelligent method for clinical locating, diagnosis and treatment of breast cancer.

Near-Infrared Spectroscopy Enables Arthroscopic Histologic Grading of Human Knee Articular Cartilage

Purpose

To develop the means to estimate cartilage histologic grades and proteoglycan content in ex vivo arthroscopy using near-infrared spectroscopy (NIRS).

Methods

In this experimental study, arthroscopic NIR spectral measurements were performed on both knees of 9 human cadavers, followed by osteochondral block extraction and in vitro measurements: reacquisition of spectra and reference measurements (proteoglycan content, and three histologic scores). A hybrid model, combining principal component analysis and linear mixed-effects model (PCA-LME), was trained for each reference to investigate its relationship with in vitro NIR spectra. The performance of the PCA-LME model was validated with ex vivo spectra before and after the exclusion of outlying spectra. Model performance was evaluated based on Spearman rank correlation (ρ) and root-mean-square error (RMSE).

Results

The PCA-LME models performed well (independent test: average ρ = 0.668, RMSE = 0.892, P < .001) in the prediction of the reference measurements based on in vitro data. The performance on ex vivo arthroscopic data was poorer but improved substantially after outlier exclusion (independent test: average ρ = 0.462 to 0.614, RMSE = 1.078 to 0.950, P = .019 to .008).

Conclusions

NIRS is capable of nondestructive evaluation of cartilage integrity (i.e., histologic scores and proteoglycan content) under similar conditions as in clinical arthroscopy.

Clinical Relevance

There are clear clinical benefits to the accurate assessment of cartilage lesions in arthroscopy. Visual grading is the current standard of care. However, optical techniques, such as NIRS, may provide a more objective assessment of cartilage damage.

Assessment of Clinical, Tissue, and Cell-Level Metrics Identify Four Biologically Distinct Knee Osteoarthritis Patient Phenotypes

OBJECTIVE

Clinical heterogeneity of primary osteoarthritis (OA) is a major challenge in understanding pathogenesis and development of targeted therapeutic strategies. This study aims to (1) identify OA patient subgroups phenotypes and (2) determine predictors of OA severity and cartilage-derived stem/progenitor concentration using clinical-, tissue-, and cell- level metrics.

DESIGN

Cartilage, synovium (SYN) and infrapatellar fatpad (IPFP) were collected from 90 total knee arthroplasty patients. Clinical metrics (patient demographics, radiograph-based joint space width (JSW), Kellgren and Lawrence score (KL)), tissue metrics (cartilage histopathology grade, glycosaminoglycans (GAGs)) and cell-based metrics (cartilage-, SYN-, and IPFP-derived cell concentration ([Cell], cells/mg), connective tissue progenitor (CTP) prevalence (PCTP, CTPs/million cells plated), CTP concentration, [CTP], CTPs/mg)) were assessed using k-mean clustering and linear regression model.

RESULTS

Four patient subgroups were identified. Clusters 1 and 2 comprised of younger, high body mass index (BMI) patients with healthier cartilage, where Cluster 1 had high CTP in cartilage, SYN, and IPFP, and Cluster 2 had low [CTP] in cartilage, SYN, and IPFP. Clusters 3 and 4 comprised of older, low BMI patients with diseased cartilage where Cluster 3 had low [CTP] in SYN, IPFP but high [CTP] in cartilage, and Cluster 4 had high [CTP] in SYN, IPFP but low [CTP] in cartilage. Age (r = 0.23, P = 0.026), JSW (r = 0.28, P = 0.007), KL (r = 0.26, P = 0.012), GAG/mg cartilage tissue (r = -0.31, P = 0.007), and SYN-derived [Cell] (r = 0.25, P = 0.049) were weak but significant predictors of OA severity. Cartilage-derived [Cell] (r = 0.38, P < 0.001) and PCTP (r = 0.9, P < 0.001) were moderate/strong predictors of cartilage-derived [CTP].

CONCLUSION

Initial findings suggests the presence of OA patient subgroups that could define opportunities for more targeted patient-specific approaches to prevention and treatment.

Resolving the Near-Infrared Spectrum of Articular Cartilage

OBJECTIVE

Spectroscopic techniques, such as near-infrared (NIR) spectroscopy, are gaining significant research interest for characterizing connective tissues, particularly articular cartilage, because there is still a largely unmet need for rapid, accurate and objective methods for assessing tissue integrity in real-time during arthroscopic surgery. This study aims to identify the NIR spectral range that is optimal for characterizing cartilage integrity by (a) identifying the contribution of its major constituents (collagen and proteoglycans) to its overall spectrum using proxy constituent models and (b) determining constituent-specific spectral contributions that can be used for assessment of cartilage in its physiological state.

DESIGN

The NIR spectra of cartilage matrix constituent models were measured and compared with specific molecular components of organic compounds in the NIR spectral range in order to identify their bands and molecular assignments. To verify the identified bands, spectra of the model compounds were compared with those of native cartilage. Since water obscures some bands in the NIR range, spectral measurements of the native cartilage were conducted under conditions of decreasing water content to amplify features of the solid matrix components. The identified spectral bands were then compared and examined in the resulting spectra of the intact cartilage samples.

RESULTS

As water was progressively eliminated from cartilage, the specific contribution of the different matrix components was observed to correspond with those identified from the proxy cartilage component models.

CONCLUSION

Spectral peaks in the regions 5500 to 6250 cm-1 and 8100 to 8600 cm-1 were identified to be effective for characterizing cartilage proteoglycan and collagen contents, respectively.

Articular cartilage optical properties in the near-infrared (NIR) spectral range vary with depth and tissue integrity

Optical properties of biological tissues in the NIR spectral range have demonstrated significant potential for in vivo diagnostic applications and are critical parameters for modelling light interaction in biological tissues. This study aims to investigate the optical properties of articular cartilage as a function of tissue depth and integrity. The results suggest consistent wavelength-dependent variation in optical properties between cartilage depth-wise zones, as well as between healthy and degenerated tissue. Also, statistically significant differences (p<0.05) in both optical properties were observed between the different cartilage depth-wise zones and as a result of tissue degeneration. When taken into account, the outcome of this study could enable accurate modelling of light interaction in cartilage matrix and could provide useful diagnostic information on cartilage integrity.

Characterization of connective tissues using near-infrared spectroscopy and imaging

Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.

Near Infrared Spectroscopy Enables Differentiation of Mechanically and Enzymatically Induced Cartilage Injuries

This study evaluates the feasibility of near infrared (NIR) spectroscopy to distinguish between different cartilage injury types associated with post-traumatic osteoarthritis and idiopathic osteoarthritis (OA) induced by mechanical and enzymatic damages. Bovine osteochondral samples (n = 72) were subjected to mechanical (n = 24) and enzymatic (n = 36) damage; NIR spectral measurements were acquired from each sample before and after damage, and from a separate control group (n = 12). Biomechanical measurements were then conducted to determine the functional integrity of the samples. NIR spectral variations resulting from different damage types were investigated and the samples classified using partial least squares discriminant analysis (PLS-DA). Partial least squares regression (PLSR) was then employed to investigate the relationship between the NIR spectra and biomechanical properties of the samples. Results of the study demonstrate that substantial spectral changes occur in the region of 1700–2200 nm due to tissue damages, while differences between enzymatically and mechanically induced damages can be observed mainly in the region of 1780–1810 nm. We conclude that NIR spectroscopy, combined with multivariate analysis, is capable of discriminating between cartilage injuries that mimic idiopathic OA and traumatic injuries based on specific spectral features. This information could be useful in determining the optimal treatment strategy during cartilage repair in arthroscopy.

Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy

Introduction

Assessment of cartilage integrity during arthroscopy is limited by the subjective visual nature of the technique. To address this shortcoming in diagnostic evaluation of articular cartilage, near infrared spectroscopy (NIRS) has been proposed. In this study, we evaluated the capacity of NIRS, combined with machine learning techniques, to classify cartilage integrity.

Methods

Rabbit (n = 14) knee joints with artificial injury, induced via unilateral anterior cruciate ligament transection (ACLT), and the corresponding contra-lateral (CL) joints, including joints from separate non-operated control (CNTRL) animals (n = 8), were used. After sacrifice, NIR spectra (1000–2500 nm) were acquired from different anatomical locations of the joints (n_TOTAL = 313: n_CNTRL = 111, n_CL = 97, n_ACLT = 105). Machine and deep learning methods (support vector machines–SVM, logistic regression–LR, and deep neural networks–DNN) were then used to develop models for classifying the samples based solely on their NIR spectra.

Results

The results show that the model based on SVM is optimal of distinguishing between ACLT and CNTRL samples (ROC_AUC = 0.93, kappa = 0.86), LR is capable of distinguishing between CL and CNTRL samples (ROC_AUC = 0.91, kappa = 0.81), while DNN is optimal for discriminating between the different classes (multi-class classification, kappa = 0.48).

Conclusion

We show that NIR spectroscopy, when combined with machine learning techniques, is capable of holistic assessment of cartilage integrity, with potential for accurately distinguishing between healthy and diseased cartilage.

Electronic supplementary material

The online version of this article (10.1007/s12195-020-00612-5) contains supplementary material, which is available to authorized users.

Arthroscopic Determination of Cartilage Proteoglycan Content and Collagen Network Structure with Near-Infrared Spectroscopy

Conventional arthroscopic evaluation of articular cartilage is subjective and insufficient for assessing early compositional and structural changes during the progression of post-traumatic osteoarthritis. Therefore, in this study, arthroscopic near-infrared (NIR) spectroscopy is introduced, for the first time, for in vivo evaluation of articular cartilage thickness, proteoglycan (PG) content, and collagen orientation angle. NIR spectra were acquired in vivo and in vitro from equine cartilage adjacent to experimental cartilage repair sites. As reference, digital densitometry and polarized light microscopy were used to evaluate superficial and full-thickness PG content and collagen orientation angle. To relate NIR spectra and cartilage properties, ensemble neural networks, each with two different architectures, were trained and evaluated by using Spearman’s correlation analysis (ρ). The ensemble networks enabled accurate predictions for full-thickness reference properties (PG content: ρ_{in vitro, Val}= 0.691, ρ_{in vivo}= 0.676; collagen orientation angle: ρ_{in vitro, Val}= 0.626, ρ_{in vivo}= 0.574) from NIR spectral data. In addition, the networks enabled reliable prediction of PG content in superficial (25%) cartilage (ρ_{in vitro, Val}= 0.650, ρ_{in vivo}= 0.613) and cartilage thickness (ρ_{in vitro, Val}= 0.797, ρ_{in vivo}= 0.596). To conclude, NIR spectroscopy could enhance the detection of initial cartilage degeneration and thus enable demarcation of the boundary between healthy and compromised cartilage tissue during arthroscopic surgery.

Near infrared spectroscopic assessment of developing engineered tissues: correlations with compositional and mechanical properties

Articular cartilage degeneration causes pain and reduces the mobility of millions of people annually. Regeneration of cartilage is challenging, due in part to its avascular nature, and thus tissue engineering approaches for cartilage repair have been studied extensively. Current techniques to assess the composition and integrity of engineered tissues, including histology, biochemical evaluation, and mechanical testing, are destructive, which limits real-time monitoring of engineered cartilage tissue development in vitro and in vivo. Near infrared spectroscopy (NIRS) has been proposed as a non-destructive technique to characterize cartilage. In the current study, we describe a non-destructive NIRS approach for assessment of engineered cartilage during development, and demonstrate correlation of these data to gold standard mid infrared spectroscopic measurements, and to mechanical properties of constructs. Cartilage constructs were generated using bovine chondrocyte culture on polyglycolic acid (PGA) scaffolds for six weeks. BMP-4 growth factor and ultrasound mechanical stimulation were used to provide a greater dynamic range of tissue properties and outcome variables. NIR spectra were collected daily using an infrared fiber optic probe in diffuse reflectance mode. Constructs were harvested after three and six weeks of culture and evaluated by the correlative modalities of mid infrared (MIR) spectroscopy, histology, and mechanical testing (equilibrium and dynamic stiffness). We found that specific NIR spectral absorbances correlated with MIR measurements of chemical composition, including relative amount of PGA (R = 0.86, p = 0.02), collagen (R = 0.88, p = 0.03), and proteoglycan (R = 0.83, p = 0.01). In addition, NIR-derived water content correlated with MIR-derived proteoglycan content (R = 0.76, p = 0.04). Both equilibrium and dynamic mechanical properties generally improved with cartilage growth from three to six weeks. In addition, significant correlations between NIRS-derived parameters and mechanical properties were found for constructs that were not treated with ultrasound (PGA (R = 0.71, p = 0.01), water (R = 0.74, p = 0.02), collagen (R = 0.69, p = 0.04), and proteoglycan (R = 0.62, p = 0.05)). These results lay the groundwork for extension to arthroscopic engineered cartilage assessment in clinical studies.

Arthroscopic near infrared spectroscopy enables simultaneous quantitative evaluation of articular cartilage and subchondral bone in vivo

Arthroscopic assessment of articular tissues is highly subjective and poorly reproducible. To ensure optimal patient care, quantitative techniques (e.g., near infrared spectroscopy (NIRS)) could substantially enhance arthroscopic diagnosis of initial signs of post-traumatic osteoarthritis (PTOA). Here, we demonstrate, for the first time, the potential of arthroscopic NIRS to simultaneously monitor progressive degeneration of cartilage and subchondral bone in vivo in Shetland ponies undergoing different experimental cartilage repair procedures. Osteochondral tissues adjacent to the repair sites were evaluated using an arthroscopic NIRS probe and significant (p < 0.05) degenerative changes were observed in the tissue properties when compared with tissues from healthy joints. Artificial neural networks (ANN) enabled reliable (ρ = 0.63–0.87, NMRSE = 8.5–17.2%, RPIQ = 1.93–3.03) estimation of articular cartilage biomechanical properties, subchondral bone plate thickness and bone mineral density (BMD), and subchondral trabecular bone thickness, bone volume fraction (BV), BMD, and structure model index (SMI) from in vitro spectral data. The trained ANNs also reliably predicted the properties of an independent in vitro test group (ρ = 0.54–0.91, NMRSE = 5.9–17.6%, RPIQ = 1.68–3.36). However, predictions based on arthroscopic NIR spectra were less reliable (ρ = 0.27–0.74, NMRSE = 14.5–24.0%, RPIQ = 1.35–1.70), possibly due to errors introduced during arthroscopic spectral acquisition. Adaptation of NIRS could address the limitations of conventional arthroscopy through quantitative assessment of lesion severity and extent, thereby enhancing detection of initial signs of PTOA. This would be of high clinical significance, for example, when conducting orthopaedic repair surgeries.

Characterizing human subchondral bone properties using near-infrared (NIR) spectroscopy

Degenerative joint conditions are often characterized by changes in articular cartilage and subchondral bone properties. These changes are often associated with subchondral plate thickness and trabecular bone morphology. Thus, evaluating subchondral bone integrity could provide essential insights for diagnosis of joint pathologies. This study investigates the potential of optical spectroscopy for characterizing human subchondral bone properties. Osteochondral samples (n = 50) were extracted from human cadaver knees (n = 13) at four anatomical locations and subjected to NIR spectroscopy. The samples were then imaged using micro-computed tomography to determine subchondral bone morphometric properties, including: plate thickness (Sb.Th), trabecular thickness (Tb.Th), volume fraction (BV/TV), and structure model index (SMI). The relationship between the subchondral bone properties and spectral data in the 1^st (650–950 nm), 2^nd (1100–1350 nm) and 3^rd (1600–1870 nm) optical windows were investigated using partial least squares (PLS) regression multivariate technique. Significant correlations (p < 0.0001) and relatively low prediction errors were obtained between spectral data in the 1^st optical window and Sb.Th (R² = 92.3%, error = 7.1%), Tb.Th (R² = 88.4%, error = 6.7%), BV/TV (R² = 83%, error = 9.8%) and SMI (R² = 79.7%, error = 10.8%). Thus, NIR spectroscopy in the 1^st tissue optical window is capable of characterizing and estimating subchondral bone properties, and can potentially be adapted during arthroscopy.

Compositional Assessment of Human Tracheal Cartilage by Infrared Spectroscopy

Objectives To assess the potential of infrared fiber-optic spectroscopy to evaluate the compositional properties of human tracheal cartilage. Study Design Laboratory-based study. Methods Twenty human cadaveric distal tracheas were harvested (age range 20-78 years; 6 females, 14 males) for compositional analysis. Histologic staining, Fourier transform infrared imaging spectroscopy data on collagen and proteoglycan (PG) content, and near-infrared (NIR) fiber-optic probe spectroscopic data that reflect protein and water content were evaluated. NIR fiber-optic probe data were also obtained from the proximal trachea in 4 human cadavers (age range 51-65 years; 2 females, 2 males) in situ for comparison to distal trachea spectral data. Results In the distal trachea cohort, the spectroscopic-determined ratio of PG/amide I, indicative of the relative amount of PG, was significantly higher in the tissues from the younger group compared to the older group (0.37 ± 0.08 vs 0.32 ± 0.05, P = .05). A principal component analysis of the NIR spectral data enabled separation of spectra based on tracheal location, likely due to differences in both protein and water content. The NIR-determined water content based on the 5200-cm-1 peak was significantly higher in the distal trachea compared to the proximal trachea ( P < .001). Conclusions Establishment of normative compositional values and further elucidating differences between the segments of trachea will enable more directed research toward appropriate compositional end points in regenerative medicine for tracheal repair.

Near-Infrared Spectroscopy Predicts Compositional and Mechanical Properties of Hyaluronic Acid-Based Engineered Cartilage Constructs

Hyaluronic acid (HA) has been widely used for cartilage tissue engineering applications. However, the optimal time point to harvest HA-based engineered constructs for cartilage repair is still under investigation. In this study, we investigated the ability of a nondestructive modality, near-infrared spectroscopic (NIR) analysis, to predict compositional and mechanical properties of HA-based engineered cartilage constructs. NIR spectral data were collected from control, unseeded constructs, and twice per week by fiber optic from constructs seeded with chondrocytes during their development over an 8-week period. Constructs were harvested at 2, 4, 6, and 8 weeks, collagen and sulfated glycosaminoglycan content measured using biochemical assays, and the mechanical properties of the constructs evaluated using unconfined compression tests. NIR absorbances associated with the scaffold material, water, and engineered cartilage matrix, were identified. The NIR-determined matrix absorbance plateaued after 4 weeks of culture, which was in agreement with the biochemical assay results. Similarly, the mechanical properties of the constructs also plateaued at 4 weeks. A multivariate partial least square model based on NIR spectral input was developed to predict the moduli of the constructs, which resulted in a prediction error of 10% and R value of 0.88 for predicted versus actual values of dynamic modulus. Furthermore, the maximum increase in moduli was calculated from the first derivative of the curve fit of NIR-predicted and actual moduli values over time, and both occurred at ∼2 weeks. Collectively, these data suggest that NIR spectral data analysis could be an alternative to destructive biochemical and mechanical methods for evaluation of HA-based engineered cartilage construct properties.

Intrinsic fluorescence and mechanical testing of articular cartilage in human patients with osteoarthritis

The degeneration of articular cartilage is the main cause of osteoarthritis (OA), a common cause of disability among elderly patients. The aim of this study is to understand the correlation between intrinsic fluorescence of articular cartilage and its biomechanical properties in patients with osteoarthritis. Cylindrical samples of articular cartilage 6 mm in diameter were extracted via biopsy punch from the femoral condyles of 6 patients with advanced OA undergoing knee replacement surgery. The mechanical stiffness and fluorescence of each cartilage plug were measured by indentation test and spectrofluorometry. Maps of fluorescence intensity, at excitation/emission wavelengths of 240-520/290-530 nm, were used to identify wavelengths of interest. The mechanical stiffness and fluorescence intensity were correlated using a Spearman analysis. The excitation/emission maps demonstrated three fluorescence peaks at excitation/emission wavelength pairs 330/390, 350/430 and 370/460 nm. The best correlation between the fluorescence intensity and stiffness of cartilage was obtained for the 330 nm excitation band [R=0.82, p=0.04]. The intrinsic fluorescence of articular cartilage may have application in optically assessing the state of cartilage in patients with osteoarthritis.

Optical spectroscopic characterization of human meniscus biomechanical properties

This study investigates the capacity of optical spectroscopy in the visible (VIS) and near-infrared (NIR) spectral ranges for estimating the biomechanical properties of human meniscus. Seventy-two samples obtained from the anterior, central, and posterior locations of the medial and lateral menisci of 12 human cadaver joints were used. The samples were subjected to mechanical indentation, then traditional biomechanical parameters (equilibrium and dynamic moduli) were calculated. In addition, strain-dependent fibril network modulus and permeability strain-dependency coefficient were determined via finite-element modeling. Subsequently, absorption spectra were acquired from each location in the VIS (400 to 750 nm) and NIR (750 to 1100 nm) spectral ranges. Partial least squares regression, combined with spectral preprocessing and transformation, was then used to investigate the relationship between the biomechanical properties and spectral response. The NIR spectral region was observed to be optimal for model development (83.0%≤R2≤90.8%). The percentage error of the models are: Eeq (7.1%), Edyn (9.6%), Eϵ (8.4%), and Mk (8.9%). Thus, we conclude that optical spectroscopy in the NIR range is a potential method for rapid and nondestructive evaluation of human meniscus functional integrity and health in real time during arthroscopic surgery.

Mechanisms of Osteoarthritic Pain. Studies in Humans and Experimental Models

Pain due to osteoarthritis (OA) is one of the most frequent causes of chronic pain. However, the mechanisms of OA pain are poorly understood. This review addresses the mechanisms which are thought to be involved in OA pain, derived from studies on pain mechanisms in humans and in experimental models of OA. Three areas will be considered, namely local processes in the joint associated with OA pain, neuronal mechanisms involved in OA pain, and general factors which influence OA pain. Except the cartilage all structures of the joints are innervated by nociceptors. Although the hallmark of OA is the degradation of the cartilage, OA joints show multiple structural alterations of cartilage, bone and synovial tissue. In particular synovitis and bone marrow lesions have been proposed to determine OA pain whereas the contribution of the other pathologies to pain generation has been studied less. Concerning the peripheral neuronal mechanisms of OA pain, peripheral nociceptive sensitization was shown, and neuropathic mechanisms may be involved at some stages. Structural changes of joint innervation such as local loss and/or sprouting of nerve fibers were shown. In addition, central sensitization, reduction of descending inhibition, descending excitation and cortical atrophies were observed in OA. The combination of different neuronal mechanisms may define the particular pain phenotype in an OA patient. Among mediators involved in OA pain, nerve growth factor (NGF) is in the focus because antibodies against NGF significantly reduce OA pain. Several studies show that neutralization of interleukin-1β and TNF may reduce OA pain. Many patients with OA exhibit comorbidities such as obesity, low grade systemic inflammation and diabetes mellitus. These comorbidities can significantly influence the course of OA, and pain research just began to study the significance of such factors in pain generation. In addition, psychologic and socioeconomic factors may aggravate OA pain, and in some cases genetic factors influencing OA pain were found. Considering the local factors in the joint, the neuronal processes and the comorbidities, a better definition of OA pain phenotypes may become possible. Studies are under way in order to improve OA and OA pain monitoring.

Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy

We demonstrate in this study the potential of near infrared (NIR) spectroscopy as a tool for monitoring progression of cartilage degeneration in an animal model. Osteoarthritic degeneration was artificially induced in one joint in laboratory rats, and the animals were sacrificed at four time points: 1, 2, 4, and 6 weeks (3 animals/week). NIR spectra were acquired from both (injured and intact) knees. Subsequently, the joint samples were subjected to histological evaluation and glycosaminoglycan (GAG) content analysis, to assess disease severity based on the Mankin scoring system and to determine proteoglycan loss, respectively. Multivariate spectral techniques were then employed for classification (principal component analysis and support vector machines) and prediction (partial least squares regression) of the samples’ Mankin scores and GAG content from their NIR spectra. Our results demonstrate that NIR spectroscopy is sensitive to degenerative changes in articular cartilage, and is capable of distinguishing between mild (weeks 1&2; Mankin <=2) and advanced (weeks 4&6; Mankin =>3) cartilage degeneration. In addition, the spectral data contains information that enables estimation of the tissue’s Mankin score (error = 12.6%, R² = 86.2%) and GAG content (error = 7.6%, R² = 95%). We conclude that NIR spectroscopy is a viable tool for assessing cartilage degeneration post-injury, such as, post-traumatic osteoarthritis.

Characterization of Articular Cartilage Recovery and Its Correlation with Optical Response in the Near-Infrared Spectral Range

OBJECTIVES

In this study, we examine the capacity of a new parameter, based on the recovery response of articular cartilage, to distinguish between healthy and damaged tissues. We also investigate whether or not this new parameter correlates with the near-infrared (NIR) optical response of articular cartilage.

DESIGN

Normal and artificially degenerated (proteoglycan-depleted) bovine cartilage samples were nondestructively probed using NIR spectroscopy. Subsequently they were subjected to a load and unloading protocol, and the recovery response was logged during unloading. The recovery parameter, elastic rebound ( E_R), is based on the strain energy released as the samples underwent instantaneous elastic recovery.

RESULTS

Our results reveal positive relationship between the rebound parameter and cartilage proteoglycan content (normal samples: 2.20 ± 0.10 N mm; proteoglycan-depleted samples: 0.50 ± 0.04 N mm for 1 hour of enzymatic treatment and 0.13 ± 0.02 N mm for 4 hours of enzymatic treatment). In addition, multivariate analysis using partial least squares regression was employed to investigate the relationship between E_R and NIR spectral data. The results reveal significantly high correlation ( R²_cal = 98.35% and R²_val = 79.87%; P < 0.0001), with relatively low error (14%), between the recovery and optical response of cartilage in the combined NIR regions 5,450 to 6,100 cm^-1 and 7,500 to 12,500 cm^-1.

CONCLUSION

We conclude that E_R can indicate the mechanical condition and state of health of articular cartilage. The correlation of E_R with cartilage optical response in the NIR range could facilitate real-time evaluation of the tissue's integrity during arthroscopic surgery and could also provide an important tool for cartilage assessment in tissue engineering and regeneration research.

Near infrared spectroscopic imaging assessment of cartilage composition: Validation with mid infrared imaging spectroscopy

Disease or injury to articular cartilage results in loss of extracellular matrix components which can lead to the development of osteoarthritis (OA). To better understand the process of disease development, there is a need for evaluation of changes in cartilage composition without the requirement of extensive sample preparation. Near infrared (NIR) spectroscopy is a chemical investigative technique based on molecular vibrations that is increasingly used as an assessment tool for studying cartilage composition. However, the assignment of specific molecular vibrations to absorbance bands in the NIR spectrum of cartilage, which arise from overtones and combinations of primary absorbances in the mid infrared (MIR) spectral region, has been challenging. In contrast, MIR spectroscopic assessment of cartilage is well-established, with many studies validating the assignment of specific bands present in MIR spectra to specific molecular vibrations. In the current study, NIR imaging spectroscopic data were obtained for compositional analysis of tissues that served as an in vitro model of OA. MIR spectroscopic data obtained from the identical tissue regions were used as the gold-standard for collagen and proteoglycan (PG) content. MIR spectroscopy in transmittance mode typically requires a much shorter pathlength through the sample (≤10 microns thick) compared to NIR spectroscopy (millimeters). Thus, this study first addressed the linearity of small absorbance bands in the MIR region with increasing tissue thickness, suitable for obtaining a signal in both the MIR and NIR regions. It was found that the linearity of specific, small MIR absorbance bands attributable to the collagen and PG components of cartilage (at 1336 and 856 cm(-1), respectively) are maintained through a thickness of 60 μm, which was also suitable for NIR data collection. MIR and NIR spectral data were then collected from 60 μm thick samples of cartilage degraded with chondroitinase ABC as a model of OA. Partial least squares (PLS) regression using NIR spectra as input predicted the MIR-determined compositional parameters of PG/collagen within 6% of actual values. These results indicate that NIR spectral data can be used to assess molecular changes that occur with cartilage degradation, and further, the data provide a foundation for future clinical studies where NIR fiber optic probes can be used to assess the progression of cartilage degradation.

Nondestructive Techniques to Evaluate the Characteristics and Development of Engineered Cartilage

In this review, methods for evaluating the properties of tissue engineered (TE) cartilage are described. Many of these have been developed for evaluating properties of native and osteoarthritic articular cartilage. However, with the increasing interest in engineering cartilage, specialized methods are needed for nondestructive evaluation of tissue while it is developing and after it is implanted. Such methods are needed, in part, due to the large inter- and intra-donor variability in the performance of the cellular component of the tissue, which remains a barrier to delivering reliable TE cartilage for implantation. Using conventional destructive tests, such variability makes it near-impossible to predict the timing and outcome of the tissue engineering process at the level of a specific piece of engineered tissue and also makes it difficult to assess the impact of changing tissue engineering regimens. While it is clear that the true test of engineered cartilage is its performance after it is implanted, correlation of pre and post implantation properties determined non-destructively in vitro and/or in vivo with performance should lead to predictive methods to improve quality-control and to minimize the chances of implanting inferior tissue.

A constituent-based preprocessing approach for characterising cartilage using NIR absorbance measurements

Near-infrared spectroscopy is a widely adopted technique for characterising biological tissues. The high dimensionality of spectral data, however, presents a major challenge for analysis. Here, we present a second-derivative Beer’s law-based technique aimed at projecting spectral data onto a lower dimension feature space characterised by the constituents of the target tissue type. This is intended as a preprocessing step to provide a physically-based, low dimensionality input to predictive models. Testing the proposed technique on an experimental set of 145 bovine cartilage samples before and after enzymatic degradation, produced a clear visual separation between the normal and degraded groups. Reduced proteoglycan and collagen concentrations, and increased water concentrations were predicted by simple linear fitting following degradation (all \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} $p\ll 0.05$ \end{document}p≪0.05). Classification accuracy using the Mahalanobis distance was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{} $\gt 98\%$ \end{document}>98% between these groups.

Optical spectroscopic determination of human meniscus composition

This study investigates the correlation between the composition of human meniscus and its absorption spectrum in the visible (VIS) and near infrared (NIR) spectral range. Meniscus samples (n = 24) were obtained from nonarthritic knees of human cadavers with no history of joint diseases. Specimens (n = 72) were obtained from three distinct sections of the meniscus, namely; anterior, center, posterior. Absorption spectra were acquired from each specimen in the VIS and NIR spectral range (400-1,100 nm). Following spectroscopic probing, the specimens were subjected to biochemical analyses to determine the matrix composition, that is water, hydroxyproline, and uronic acid contents. Multivariate analytical techniques, including principal component analysis (PCA) and partial least squares (PLS) regression, were then used to investigate the correlation between the matrix composition and it spectral response. Our results indicate that the optical absorption of meniscus matrix is related to its composition, and this relationship is optimal in the NIR spectral range (750-1,100 nm). High correlations (R(2) (uronic)  = 86.9%, R(2) (water)  = 83.8%, R(2) (hydroxyproline)  = 81.7%, p < 0.0001) were obtained between the spectral predicted and measured meniscus composition, thus suggesting that spectral data in the NIR range can be utilized for estimating the matrix composition of human meniscus. In conclusion, optical spectroscopy, particularly in the NIR spectral range, is a potential method for evaluating the composition of human meniscus. This presents a promising technique for rapid and nondestructive evaluation of meniscus integrity in real-time during arthroscopic surgery.

Electromechanical Assessment of Human Knee Articular Cartilage with Compression-Induced Streaming Potentials

PURPOSE

To assess the electromechanical properties of human knee articular cartilage with compression-induced streaming potentials for reliability among users and correlation with macroscopic and histological evaluation tools and sulfated glycosaminoglycan (sGAG) content.

METHODS

Streaming potentials are induced in cartilage in response to loading when mobile positive ions in the interstitial fluid temporarily move away from negatively charged proteoglycans. Streaming potential integrals (SPIs) were measured with an indentation probe on femoral condyles of 10 human knee specimens according to a standardized location scheme. Interobserver reliability was measured using an interclass correlation coefficient (ICC). The learning curves of 3 observers were evaluated by regression analysis. At each SPI measurement location the degradation level of the tissue was determined by means of the International Cartilage Repair Society (ICRS) score, Mankin score, and sGAG content.

RESULTS

The computed ICC was 0.77 (0.70-0.83) indicating good to excellent linear agreement of SPI values among the 3 users. A significant positive linear correlation of the learning index values was observed for 2 of the 3 users. Statistically significant negative correlations between SPI and both ICRS and Mankin scores were observed (r = 0.502, P < 0.001, and r = 0.255, P = 0.02, respectively). No correlation was observed between SPI and sGAG content (r = 0.004, P = 0.973).

CONCLUSIONS

SPI values may be used as a quantitative means of cartilage evaluation with sufficient reliability among users. Due to the significant learning curve, adequate training should be absolved before routine use of the technique.

Wavelength-dependent penetration depth of near infrared radiation into cartilage

Articular cartilage is a hyaline cartilage that lines the subchondral bone in the diarthrodial joints. Near infrared (NIR) spectroscopy is emerging as a nondestructive modality for the evaluation of cartilage pathology; however, studies regarding the depth of penetration of NIR radiation into cartilage are lacking. The average thickness of human cartilage is about 1-3 mm, and it becomes even thinner as OA progresses. To ensure that spectral data collected is restricted to the tissue of interest, i.e. cartilage in this case, and not from the underlying subchondral bone, it is necessary to determine the depth of penetration of NIR radiation in different wavelength (frequency) regions. In the current study, we establish how the depth of penetration varies throughout the NIR frequency range (4000-10 000 cm(-1)). NIR spectra were collected from cartilage samples of different thicknesses (0.5 mm to 5 mm) with and without polystyrene placed underneath. A separate NIR spectrum of polystyrene was collected as a reference. It was found that the depth of penetration varied from ∼1 mm to 2 mm in the 4000-5100 cm(-1) range, ∼3 mm in the 5100-7000 cm(-1) range, and ∼5 mm in the 7000-9000 cm(-1) frequency range. These findings suggest that the best NIR region to evaluate cartilage with no subchondral bone contribution is in the range of 4000-7000 cm(-1).

Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition

This study investigates the relationship between the optical response of human articular cartilage in the visible (VIS) and near infrared (NIR) spectral range and its matrix properties.Full-thickness osteochondral cores (dia. = 16 mm, n = 50) were extracted from human cadaver knees (N = 13) at four anatomical locations and divided into quadrants. Absorption spectra were acquired in the spectral range 400-1100 nm from one quadrant. Reference biomechanical, biochemical composition, histological, and cartilage thickness measurements were obtained from two other quadrants. A multivariate statistical technique based on partial least squares (PLS) regression was then employed to investigate the correlation between the absorption spectra and tissue properties.Our results demonstrate that cartilage optical response correlates with its function, composition and morphology, as indicated by the significant relationship between spectral predicted and measured biomechanical (79.0%  ⩽  R(2)  ⩽  80.3%, p  <  0.0001), biochemical (65.1%  ⩽  R(2)  ⩽  81.0%, p  < 0.0001), and histological scores ([Formula: see text] = 83.3%, p  < 0.0001) properties. Significant correlation was also obtained with the non-calcified cartilage thickness ([Formula: see text] = 83.2%, p  <  0.0001).We conclude that optical absorption of human cartilage in the VIS and NIR spectral range correlates with the overall tissue properties, thus providing knowledge that could facilitate development of systems for rapid assessment of tissue integrity.

Monitoring the Progression of Spontaneous Articular Cartilage Healing with Infrared Spectroscopy

OBJECTIVE

Evaluation of early compositional changes in healing articular cartilage is critical for understanding tissue repair and for therapeutic decision-making. Fourier transform infrared imaging spectroscopy (FT-IRIS) can be used to assess the molecular composition of harvested repair tissue. Furthermore, use of an infrared fiber-optic probe (IFOP) has the potential for translation to a clinical setting to provide molecular information in situ. In the current study, we determined the feasibility of IFOP assessment of cartilage repair tissue in a rabbit model, and assessed correlations with gold-standard histology.

DESIGN

Bilateral osteochondral defects were generated in mature white New Zealand rabbits, and IFOP data obtained from defect and adjacent regions at 2, 4, 6, 8, 12, and 16 weeks postsurgery. Tissues were assessed histologically using the modified O'Driscoll score, by FT-IRIS, and by partial least squares (PLS) modeling of IFOP spectra.

RESULTS

The FT-IRIS parameters of collagen content, proteoglycan content, and collagen index correlated significantly with modified O'Driscoll score (P = 0.05, 0.002, and 0.02, respectively), indicative of their sensitivity to tissue healing. Repair tissue IFOP spectra were distinguished from normal tissue IFOP spectra in all samples by PLS analysis. However, the PLS model for prediction of histological score had a high prediction error, which was attributed to the spectral information being acquired from the tissue surface only.

CONCLUSION

The strong correlations between FT-IRIS data and histological score support further development of the IFOP technique for clinical applications, although further studies to optimize data collection from the full sample depths are required.

Spatial mapping of proteoglycan content in articular cartilage using near-infrared (NIR) spectroscopy

Diagnosis of articular cartilage pathology in the early disease stages using current clinical diagnostic imaging modalities is challenging, particularly because there is often no visible change in the tissue surface and matrix content, such as proteoglycans (PG). In this study, we propose the use of near infrared (NIR) spectroscopy to spatially map PG content in articular cartilage. The relationship between NIR spectra and reference data (PG content) obtained from histology of normal and artificially induced PG-depleted cartilage samples was investigated using principal component (PC) and partial least squares (PLS) regression analyses. Significant correlation was obtained between both data (R(2) = 91.40%, p<0.0001). The resulting correlation was used to predict PG content from spectra acquired from whole joint sample, this was then employed to spatially map this component of cartilage across the intact sample. We conclude that NIR spectroscopy is a feasible tool for evaluating cartilage contents and mapping their distribution across mammalian joint.

Assessment of hyaline cartilage matrix composition using near infrared spectroscopy

Changes in the composition of the extracellular matrix (ECM) are characteristic of injury or disease in cartilage tissue. Various imaging modalities and biochemical techniques have been used to assess the changes in cartilage tissue but lack adequate sensitivity, or in the case of biochemical techniques, result in destruction of the sample. Fourier transform near infrared (FT-NIR) spectroscopy has shown promise for the study of cartilage composition. In the current study NIR spectroscopy was used to identify the contributions of individual components of cartilage in the NIR spectra by assessment of the major cartilage components, collagen and chondroitin sulfate, in pure component mixtures. The NIR spectra were obtained using homogenous pellets made by dilution with potassium bromide. A partial least squares (PLS) model was calculated to predict composition in bovine cartilage samples. Characteristic absorbance peaks between 4000 and 5000 cm(-1) could be attributed to components of cartilage, i.e. collagen and chondroitin sulfate. Prediction of the amount of collagen and chondroitin sulfate in tissues was possible within 8% (w/dw) of values obtained by gold standard biochemical assessment. These results support the use of NIR spectroscopy for in vitro and in vivo applications to assess matrix composition of cartilage tissues, especially when tissue destruction should be avoided.

Near infrared spectroscopy for rapid determination of Mankin score components: a potential tool for quantitative characterization of articular cartilage at surgery

PURPOSE

The purpose of this study was to demonstrate the potential of near infrared (NIR) spectroscopy for characterizing the health and degenerative state of articular cartilage based on the components of the Mankin score.

METHODS

Three models of osteoarthritic degeneration induced in laboratory rats by anterior cruciate ligament (ACL) transection, meniscectomy (MSX), and intra-articular injection of monoiodoacetate (1 mg) (MIA) were used in this study. Degeneration was induced in the right knee joint; each model group consisted of 12 rats (N = 36). After 8 weeks, the animals were euthanized and knee joints were collected. A custom-made diffuse reflectance NIR probe of 5-mm diameter was placed on the tibial and femoral surfaces, and spectral data were acquired from each specimen in the wave number range of 4,000 to 12,500 cm(-1). After spectral data acquisition, the specimens were fixed and safranin O staining (SOS) was performed to assess disease severity based on the Mankin scoring system. Using multivariate statistical analysis, with spectral preprocessing and wavelength selection technique, the spectral data were then correlated to the structural integrity (SI), cellularity (CEL), and matrix staining (SOS) components of the Mankin score for all the samples tested.

RESULTS

ACL models showed mild cartilage degeneration, MSX models had moderate degeneration, and MIA models showed severe cartilage degenerative changes both morphologically and histologically. Our results reveal significant linear correlations between the NIR absorption spectra and SI (R(2) = 94.78%), CEL (R(2) = 88.03%), and SOS (R(2) = 96.39%) parameters of all samples in the models. In addition, clustering of the samples according to their level of degeneration, with respect to the Mankin components, was also observed.

CONCLUSIONS

NIR spectroscopic probing of articular cartilage can potentially provide critical information about the health of articular cartilage matrix in early and advanced stages of osteoarthritis (OA).

CLINICAL RELEVANCE

This rapid nondestructive method can facilitate clinical appraisal of articular cartilage integrity during arthroscopic surgery.

Near-infrared spectroscopy correlates with established histological scores in a miniature pig model of cartilage regeneration

Near-Infrared Spectroscopy (NIRS) could be of clinical relevance in modern cartilage regeneration.In a miniature pig model correlation of measurements and histologic scores have never been used before. The data analysis was part of an animal project that investigated the effects of seeding a chondrogenic and osteogenic scaffold with a bone-marrow-derived cell concentrate and reports the histological and mechanical properties. We created 20 osteochondral defects in the femoral condyles of 10 miniature pigs.The defects were left empty (E), filled with the grafted cylinder upside down (U), or with a combined scaffold (S) containing a spongy bone cylinder covered with a collagen membrane. In the fourth group, the same scaffolds were implanted but seeded with a stem cell concentrate (S+BMCC). The animals were euthanized after 3 months, and histologic and spectrometric analyses were performed. NIRS measurements were significantly higher in the central area of the defects of group S+BMCC compared to the central area of the defects of group U. In all groups, a correlation between NIRS and the histologic scores could be demonstrated though on different levels. In the central area, a good NIRS measurement correlates with low (good) histologic scores. In group E and group S, this negative correlation was significant (p=0.01). For the first time, NIRS was successfully used to evaluate osteochondral constructs in a miniature pig model.

The contribution of bone and cartilage to the near-infrared spectrum of osteochondral tissue

Near-infrared (NIR) spectroscopy has been used to assess hyaline cartilage quality in human and animal osteochondral tissues. However, due to the lack of NIR signal from bone phosphate and the relatively deep penetration depth of the radiation, the separate contributions of cartilage and bone to the spectral signatures have not been well defined. The objectives of the current study were (1) to improve the understanding of the contributions of bone and cartilage to NIR spectra acquired from osteochondral tissue and (2) to assess the ability of this nondestructive method to predict cartilage thickness and modified Mankin grade of human tibial plateau articular cartilage. Near-infrared spectra were acquired from samples of bovine bone and cartilage with varying thicknesses and from 22 tibial plateaus harvested from patients undergoing knee replacement surgery. The spectra were recorded from regions of the tibial plateaus with varying degrees of degradation, and the cartilage thickness and modified Mankin grade of these regions were assessed histologically. The spectra from bone and cartilage samples of known thicknesses were investigated to identify spectral regions that were distinct for these two tissues. Univariate and multivariate linear regression methods were used to correlate modified Mankin grade and cartilage thickness with NIR spectral changes. The ratio of the NIR absorbances associated with water at 5270 and 7085 cm(-1) was the best differentiator of cartilage and bone spectra. The NIR prediction models for thickness and Mankin grade calculated using partial least squares regression were more accurate than were univariate-based prediction models, with a root mean square errors of cross-validation of 0.42 mm (for thickness) and 1.3 (for modified Mankin grade). We conclude that NIR spectroscopy may be used to simultaneously assess articular cartilage thickness and modified Mankin grade, based in part on differences in spectral contributions from bone and cartilage.

The innervation of synovium of human osteoarthritic joints in comparison with normal rat and sheep synovium

OBJECTIVE

To study whether osteoarthritis (OA) in the knee is associated with a change of the innervation pattern in the synovial layer.

DESIGN

In synovial tissue from the normal knee joint of rat and sheep we studied the presence of vessels and of nerve fibres using transmission electron microscopy and immunohistochemistry. Synovial material was also obtained from patients who underwent total knee replacement surgery. This material was examined for inflammatory changes, and the presence of vessels and nerve fibres was assessed.

RESULTS

The synovium in the parapatellar region of the normal knee joint of rat and sheep exhibited a dense capillary and neuronal network. It was entered by calcitonin gene-related peptide containing sensory fibres and tyrosine hydroxylase-positive sympathetic nerve fibres. Synovial material from patients with knee OA exhibited different degrees of inflammation. Synovial material without inflammation exhibited a similar vascular and neuronal network as the normal knee joint from rat and sheep. However, in synovium with inflammatory changes we found a significant decrease of nerve fibres in depth ranges close to the synovial lining layer depending on the degree of inflammation whereas deeper regions were less affected.

CONCLUSIONS

Inflammatory changes in the synovium of OA joints are associated with a massive destruction of the capillary and neuronal network which is present in normal synovium. Due to the disappearance of the sensory fibres it is unlikely that OA pain is initiated directly in the synovium. The loss of normally innervated vascularisation may have multiple consequences for the physiological functions of the synovium.

Near infrared spectroscopic evaluation of water in hyaline cartilage

In diseased conditions of cartilage such as osteoarthritis, there is typically an increase in water content from the average normal of 60-85% to greater than 90%. As cartilage has very little capability for self-repair, methods of early detection of degeneration are required, and assessment of water could prove to be a useful diagnostic method. Current assessment methods are either destructive, time consuming, or have limited sensitivity. Here, we investigated the hypotheses that non-destructive near infrared spectroscopy (NIRS) of articular cartilage can be used to differentiate between free and bound water, and to quantitatively assess water content. The absorbances centered at 5200 and 6890 cm(-1) were attributed to a combination of free and bound water, and to free water only, respectively. The integrated areas of both absorbance bands were found to correlate linearly with the absolute water content (R = 0.87 and 0.86) and with percent water content (R = 0.97 and 0.96) of the tissue. Partial least square models were also successfully developed and were used to predict water content, and percent free water. These data demonstrate that NIRS can be utilized to quantitatively determine water content in articular cartilage, and may aid in early detection of degenerative tissue changes in a laboratory setting, and with additional validations, possibly in a clinical setting.

Traumatic and degenerative cartilage lesions: arthroscopic differentiation using near-infrared spectroscopy (NIRS)

INTRODUCTION

Cartilage lesions or defects are the most common finding during knee arthroscopy. During arthroscopy, it is often difficult to differentiate between degenerative and traumatic cartilage lesions. The study aimed to determine the impact of near-infrared spectroscopy (NIRS) on the distinction between traumatic and degenerative cartilage lesions in the medial femoral condyle (MFC). It was hypothesized that NIRS as able to distinguish between traumatic and degenerative cartilage lesions.

MATERIALS AND METHODS

Arthroscopic evaluation was performed in six patients who had undergone anterior cruciate ligament (ACL) reconstruction and in six patients who had undergone high tibial osteotomy (HTO). In both groups, a grade III cartilage lesion was present within the MFC. NIRS evaluation was performed with a special probe (arthrospec-one, Arthrospec GmbH, Jena, Germany). NIRS measurements produced semi-quantitative values ranging from 0 (heavily degenerated cartilage) to 100 (completely intact cartilage).

RESULTS

The mean near-infrared-light absorption within the traumatic lesions in the MFC of the ACL group was 71.5 (range 61-80). In the HTO patients, this value was significantly (p < 0.001) lower at 31.7 (range 31-33). The margin of the MFC outside the lesion in the ACL group had the same adsorption as the lesion (p = 0.549).

CONCLUSION

After an injury, cartilage has a normal or nearly normal absorbance on near-infrared-light. Thus, it is possible to distinguish intraoperatively between traumatic and degenerative lesions. In addition, our results demonstrate that evaluating cartilage with NIRS is a dependable method for improving the diagnosis of significant chondral lesions.

Near infrared (NIR) absorption spectra correlates with subchondral bone micro-CT parameters in osteoarthritic rat models

Determining the properties and integrity of subchondral bone in the developmental stages of osteoarthritis, especially in a form that can facilitate real-time characterization for diagnostic and decision-making purposes, is still a matter for research and development. This paper presents relationships between near infrared absorption spectra and properties of subchondral bone obtained from 3 models of osteoarthritic degeneration induced in laboratory rats via: (i) menisectomy (MSX); (ii) anterior cruciate ligament transaction (ACL); and (iii) intra-articular injection of mono-ido-acetate (1mg) (MIA), in the right knee joint, with 12 rats per model group (N=36). After 8weeks, the animals were sacrificed and knee joints were collected. A custom-made diffuse reflectance NIR probe of diameter 5mm was placed on the tibial surface and spectral data were acquired from each specimen in the wavenumber range 4000-12500cm(-1). After spectral acquisition, micro computed tomography (micro-CT) was performed on the samples and subchondral bone parameters namely: bone volume (BV) and bone mineral density (BMD) were extracted from the micro-CT data. Statistical correlation was then conducted between these parameters and regions of the near infrared spectra using multivariate techniques including principal component analysis (PCA), discriminant analysis (DA), and partial least squares (PLS) regression. Statistically significant linear correlations were found between the near infrared absorption spectra and subchondral bone BMD (R(2)=98.84%) and BV (R(2)=97.87%). In conclusion, near infrared spectroscopic probing can be used to detect, qualify and quantify changes in the composition of the subchondral bone, and could potentially assist in distinguishing healthy from OA bone as demonstrated with our laboratory rat models.

Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra

The conventional mechanical properties of articular cartilage, such as compressive stiffness, have been demonstrated to be limited in their capacity to distinguish intact (visually normal) from degraded cartilage samples. In this paper, we explore the correlation between a new mechanical parameter, namely the reswelling of articular cartilage following unloading from a given compressive load, and the near infrared (NIR) spectrum. The capacity to distinguish mechanically intact from proteoglycan-depleted tissue relative to the "reswelling" characteristic was first established, and the result was subsequently correlated with the NIR spectral data of the respective tissue samples. To achieve this, normal intact and enzymatically degraded samples were subjected to both NIR probing and mechanical compression based on a load-unload-reswelling protocol. The parameter δr, characteristic of the osmotic "reswelling" of the matrix after unloading to a constant small load in the order of the osmotic pressure of cartilage, was obtained for the different sample types. Multivariate statistics was employed to determine the degree of correlation between δr and the NIR absorption spectrum of relevant specimens using Partial Least Squared (PLS) regression. The results show a strong relationship (R(2)=95.89%, p<0.0001) between the spectral data and δr. This correlation of δr with NIR spectral data suggests the potential for determining the reswelling characteristics non-destructively. It was also observed that δr values bear a significant relationship with the cartilage matrix integrity, indicated by its proteoglycan content, and can therefore differentiate between normal and artificially degraded proteoglycan-depleted cartilage samples. It is therefore argued that the reswelling of cartilage, which is both biochemical (osmotic) and mechanical (hydrostatic pressure) in origin, could be a strong candidate for characterizing the tissue, especially in regions surrounding focal cartilage defects in joints.

Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin score

OBJECTIVE

The aim of this study was to demonstrate the potential of near-infrared (NIR) spectroscopy for categorizing cartilage degeneration induced in animal models.

METHOD

Three models of osteoarthritic degeneration were induced in laboratory rats via one of the following methods: (1) menisectomy (MSX); (2) anterior cruciate ligament transection (ACLT); and (3) intra-articular injection of mono-ido-acetate (1 mg) (MIA), in the right knee joint, with 12 rats per model group. After 8 weeks, the animals were sacrificed and tibial knee joints were collected. A custom-made near-infrared (NIR) probe of diameter 5 mm was placed on the cartilage surface and spectral data were acquired from each specimen in the wave number range 4,000-12,500 cm(-1). Following spectral data acquisition, the specimens were fixed and Safranin-O staining was performed to assess disease severity based on the Mankin scoring system. Using multivariate statistical analysis based on principal component analysis and partial least squares regression, the spectral data were then related to the Mankin scores of the samples tested.

RESULTS

Mild to severe degenerative cartilage changes were observed in the subject animals. The ACLT models showed mild cartilage degeneration, MSX models moderate, and MIA severe cartilage degenerative changes both morphologically and histologically. Our result demonstrates that NIR spectroscopic information is capable of separating the cartilage samples into different groups relative to the severity of degeneration, with NIR correlating significantly with their Mankin score (R(2) = 88.85%).

CONCLUSION

We conclude that NIR is a viable tool for evaluating articular cartilage health and physical properties such as change in thickness with degeneration.

Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage

The determination of the characteristics of articular cartilage such as thickness, stiffness and swelling, especially in the form that can facilitate real-time decisions and diagnostics is still a matter for research and development. This paper correlates near infrared spectroscopy with mechanically measured cartilage thickness to establish a fast, non-destructive, repeatable and precise protocol for determining this tissue property. Statistical correlation was conducted between the thickness of bovine cartilage specimens (n=97) and regions of their near infrared spectra. Nine regions were established along the full absorption spectrum of each sample and were correlated with the thickness using partial least squares (PLS) regression multivariate analysis. The coefficient of determination (R²) varied between 53 and 93%, with the most predictive region (R²=93.1%, p<0.0001) for cartilage thickness lying in the region (wavenumber) 5350-8850 cm⁻¹. Our results demonstrate that the thickness of articular cartilage can be measured spectroscopically using NIR light. This protocol is potentially beneficial to clinical practice and surgical procedures in the treatment of joint disease such as osteoarthritis.

ISSUES AND ADVANCES IN THE EARLY STAGE DIAGNOSIS OF OSTEOARTHRITIS

With the progress of localized treatment procedures such as unicompartmental knee replacement, chondrocyte implantation and osteochondral grafting, it has become important to develop a means of assessing early stage cartilage and bone degradation. This review outlines the recent advances in arthroscopic tools, and discusses the major problems and issues faced in developing effective assessment methods. The central problem in joint tissue assessment is to discriminate degradation from the wide variation in normal tissue. This discrimination, however, is far from being realized by current methodologies, and is compounded by the difficulty in correlating structural features with pain and mobility in the joint. In response to these findings, an argument is provided for a new direction in quantitative tissue evaluation using an integrated chemical, structural, and functional approach, and the importance of structure–function–pain relationships.

Near-infrared spectroscopy for arthroscopic evaluation of cartilage lesions: results of a blinded, prospective, interobserver study

BACKGROUND

Mechanical tests to grade cartilage damage are limited by the instruments used and by the ability to access all areas of cartilage within a joint. Better methods to diagnose cartilage injury or degeneration are needed. Purpose/

HYPOTHESIS

To detect the interobserver variance of arthroscopic cartilage grading by subjective judgment using the International Cartilage Repair Society (ICRS) score and by objective measurement using near-infrared (NIR) spectroscopy. We hypothesized that objective measurement of cartilage lesions by NIR spectroscopy will yield more valid results than routine grading using the ICRS score.

STUDY DESIGN

Cohort study (diagnosis); Level of evidence, 2.

METHODS

Fifteen patients undergoing arthroscopic knee operations were evaluated by 4 experienced arthroscopists independently. The cartilage lesions within the medial knee compartment were estimated by each observer using the ICRS grade and by measurements with a special arthroscopic NIR spectroscopy probe.

RESULTS

The ICRS grading had a poor interobserver agreement, with a mean Fleiss kappa index of κ = 0.173. Only in 10% (6 of 60) of judged cartilage areas did all 4 surgeons grade the cartilage areas with the same result. In 17 areas (28.3%), the surgeons had a variance of 2 or more grades. In the remaining cases, the surgeons varied within 1 grade. The objective NIR spectroscopy-obtained measurements of cartilage resulted in a significant correlation within the observers of R = 0.885 ± 0.036 (P < .001).

CONCLUSION

Our results of interobserver evaluation in real-time arthroscopic cartilage grading suggest that this subjective grading is not satisfactory. This study emphasizes the need for objective measurement techniques for arthroscopic cartilage grading. Near-infrared spectroscopy has a good interobserver correlation. Thus, this method could be developed in the future as a precise method of measuring cartilage lesions.

Nondestructive assessment of engineered cartilage constructs using near-infrared spectroscopy

Noninvasive assessment of engineered cartilage properties would enable better control of the developing tissue towards the desired structural and compositional endpoints through optimization of the biochemical environment in real time. The objective of this study is to assess the matrix constituents of cartilage using near-infrared spectroscopy (NIRS), a technique that permits full-depth assessment of developing engineered tissue constructs. Mid-infrared (mid-IR) and NIR data were acquired from full-thickness cartilage constructs that were grown up to 4 weeks with and without mechanical stimulation. Correlations were assessed between established mid-IR peak areas that reflect the relative amount of collagen (amide I, amide II, and 1338 cm(-1)) and proteoglycan (PG), (850 cm(-1)), and the integrated area of the NIR water absorbance at 5190 cm(-1). This analysis was performed to evaluate whether simple assessment of the NIR water absorbance could yield information about matrix development. It was found that an increase in the mid-IR PG absorbance at 850 cm(-1) correlated with the area of the NIR water peak (Spearman's rho = 0.95, p < 0.0001). In the second analysis, a partial least squares method (PLS1) was used to assess whether an extended NIR spectral range (5400-3800 cm(-1)) could be utilized to predict collagen and proteoglycan content of the constructs based on mid-IR absorbances. A subset of spectra was randomly selected as an independent prediction set in this analysis. Average of the normalized root mean square errors of prediction of first-derivative NIR spectral models were 7% for 850 cm(-1) (PG), 11% for 1338 cm(-1) (collagen), 8% for amide II (collagen), and 8% for amide I (collagen). These results demonstrate the ability of NIRS to monitor macromolecular content of cartilage constructs and is the first step towards employing NIR to assess engineered cartilage in situ.

How do visual, spectroscopic and biomechanical changes of cartilage correlate in osteoarthritic knee joints?

BACKGROUND

Characteristic changes in cartilage of human knee joints with different degrees of osteoarthritis (OA) have been investigated by visual, biophotonical and biomechanical examination. Knowledge about the cartilage composition and changes during the development of OA is important for diagnostic decisions and understanding the pathogenesis of OA.

METHODS

Thirty two patients with severe knee OA received endoprosthetic replacement. During surgical intervention cartilage specimen were harvested from defined surface areas of the joints. The degree of cartilage defects was classified visually (ICRS Grade: International Cartilage Repair Society), biophotonically (NIRS: near infrared spectroscopy) and biomechanically (Young's Modulus). To characterise links between the investigated parameters the Spearman's rank correlation coefficient was used.

FINDINGS

Significant negative correlations were found between visual macroscopic degree of degeneration (ICRS Grade) and biophotonic characteristics (NIRS) (rho=-0.467) or cartilage stiffness (Young's Modulus) (rho=-0.501). Between NIRS and Young's Modulus significant positive correlation of rho=0.535 was detected.

INTERPRETATION

Visual, biophotonic and biomechanical properties of cartilage reveal strong correlations in all degrees of cartilage defects in patients with severe OA. According to these results, we indicate that an objective, non-invasive and non-destructive measurement of cartilage properties during open and arthroscopic knee surgery is possible by NIRS and provide a novel tool to evaluate disease intervention and treatment.

Perilesional changes of focal osteochondral defects in an ovine model and their relevance to human osteochondral injuries

Perilesional changes of chronic focal osteochondral defects were assessed in the knees of 23 sheep. An osteochondral defect was created in the main load-bearing region of the medial condyle of the knees in a controlled, standardised manner. The perilesional cartilage was evaluated macroscopically and biopsies were taken at the time of production of the defect (T0), during a second operation one month later (T1), and after killing animals at three (T3; n = 8), four (T4; n = 8), and seven (T7; n = 8) months. All the samples were histologically assessed by the International Cartilage Repair Society grading system and Mankin histological scores. Biopsies were taken from human patients (n = 10) with chronic articular cartilage lesions and compared with the ovine specimens. The ovine perilesional cartilage presented with macroscopic and histological signs of degeneration. At T1 the International Cartilage Repair Society ‘Subchondral Bone’ score decreased from a mean of 3.0 (sd 0) to a mean of 1.9 (sd 0.3) and the ‘Matrix’ score from a mean of 3.0 (sd 0) to a mean of 2.5 (sd 0.5). This progressed further at T3, with the International Cartilage Repair Society ‘Surface’ grading, the ‘Matrix’ grading, ‘Cell Distribution’ and ‘Cell Viability’ grading further decreasing and the Mankin score rising from a mean of 1.3 (sd 1.4) to a mean of 5.1 (sd 1.6). Human biopsies achieved Mankin grading of a mean of 4.2 (sd 1.6) and were comparable with the ovine histology at T1 and T3.

The perilesional cartilage in the animal model became chronic at one month and its histological appearance may be considered comparable with that seen in human osteochondral defects after trauma.

Detection and evaluation of initial cartilage pathology in man: A comparison between MRT, arthroscopy and near-infrared spectroscopy (NIR) in their relation to initial knee pain

BACKGROUND AND AIMS

MRI and arthroscopy are important methods in the evaluation of cartilage pathology. But frequently initial changes of cartilage in combination with chronic knee pain cannot be detected by employing these two methods. Better diagnostic tools for the detection of the early stages of osteoarthritis (OA) are required. The objective of this study was to show that near-infrared spectroscopy (NIRS) can be incorporated into routine arthroscopy to improve detection and assessment of the initial cartilage pathology. Furthermore correlations between findings in MRI, arthroscopy and NIRS in patients with initial symptoms of OA have studied.

METHODS

Patients (n=21, 12 women, 9 men, age: 15-59 years, mean 34.19 years) with knee pain lasting for at least half a year without any trauma of the knee in their history were interviewed (body weight, smoking behaviour) and clinically evaluated using the Knee Injury and Osteoarthritis Outcome Score (KOOS). Also serum parameters (cholesterol, lipids) were analysed, conventional X-rays in three directions (evaluated according to Kellgren and Lawrence) and MRI (evaluation of cartilage damage according to the ICRS-score) were performed preoperatively in all patients. During subsequent arthroscopy cartilage damage was evaluated according to the ICRS-score. In addition the spectral reflection of cartilage was investigated in all knees using a special micro-glass-fiber probe in the near-infrared light region (spectral range between 1150 and 1475nm). To characterize relations between the investigated parameters the Spearman's rank correlation coefficient was used. Inter-observer variance was calculated employing the Cohens-Kappa-test.

RESULTS

MRI demonstrated a strong inter-observer variance with no significant correlations to other parameters. The same was observed for arthroscopic findings. Only NIRS showed significant correlations with three out of five KOOS subscores. Within the general parameters only smoking behaviour showed a significant correlation with two of the KOOS-scores. NIRS therefore seemed to be a sensitive diagnostic tool in detection of initial pathology in human cartilage. The additional necessary time for the spectroscopic investigation as part of the routine arthroscopy ranged between 3 and 7min (mean: 4min 18s).

CONCLUSION

Particularly for early-stage cartilage lesions (ICRS 0/I) MRI and arthroscopy have rather low predictive value. The inter-observer variance is very high (Cohens-Kappa<0.4). Correlations found between NIRS and KOOS suggest that NIRS potentially can be used for detection of initial cartilage pathology and may be helpful in the evaluation of the benefit of different medical or surgical interventions at early-stage of articular cartilage damage.