Classification of histologically scored human knee osteochondral plugs by quantitative analysis of magnetic resonance images at 3T

Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction

This review presents the changes that the imaging of articular cartilage has undergone throughout the last decades. It highlights that the expectation is no longer to image the structure and associated functions of articular cartilage but, instead, to devise methods for generating non-invasive, function-depicting images with quantitative information that is useful for detecting the early, pre-clinical stage of diseases such as primary or post-traumatic osteoarthritis (OA/PTOA). In this context, this review summarizes (a) the structure and function of articular cartilage as a molecular imaging target, (b) quantitative MRI for non-invasive assessment of articular cartilage composition, microstructure, and function with the current state of medical diagnostic imaging, (c), non-destructive imaging methods, (c) non-destructive quantitative articular cartilage live-imaging methods, (d) artificial intelligence (AI) classification of degeneration and prediction of OA progression, and (e) our contribution to this field, which is an AI-supported, non-destructive quantitative optical biopsy for early disease detection that operates on a digital tissue architectural fingerprint. Collectively, this review shows that articular cartilage imaging has undergone profound changes in the purpose and expectations for which cartilage imaging is used; the image is becoming an AI-usable biomarker with non-invasive quantitative functional information. This may aid in the development of translational diagnostic applications and preventive or early therapeutic interventions that are yet beyond our reach.

Evaluation of cartilage degeneration using multiparametric quantitative ultrashort echo time-based MRI: an ex vivo study

BACKGROUND

The quantitative MR techniques developed rapidly, vary MR-biomarkers have shown the ability to assess the quality of articular cartilage. This study aimed to investigate the diagnostic efficacy of multi-parametric quantitative ultrashort echo time (UTE)-based MRI for evaluating human cartilage degeneration.

METHODS

Twenty fresh anterolateral femoral condyle samples were obtained from 20 patients (age, 58.8±6.6 years; 6 females) who underwent total knee arthroplasty due to primary osteoarthritis (OA). The samples were imaged using UTE-based magnetization transfer (UTE-MT), UTE-based adiabatic T1ρ (UTE-AdiabT1ρ), UTE-based T2* (UTE-T2*), and CubeQuant-T2 sequences. Cartilage degeneration was classified based on the OA Research Society International grade and polarized light microscopy (PLM) collagen organization score. Spearman's correlation analysis was used to determine the relationships between quantitative MRI biomarkers [UTE-MT ratio (UTE-MTR), UTE-AdiabT1ρ, UTE-T2*, and CubeQuant-T2], OA Research Society International grade, and PLM collagen organization score. The diagnostic efficacy of each MRI biomarker for the detection of mild cartilage degeneration was assessed using the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

Of the quantitative MRI biomarkers, UTE-MTR had the strongest correlation with both OA Research Society International grade (r=-0.709, P<0.001) and PLM collagen organization score (r=0.579, P<0.001). The UTE-MTR and UTE-AdiabT1ρ values showed significant differences between the normal group and the mild degeneration group (P=0.047 and 0.015, respectively), while UTE-T2* and CubeQuant-T2 did not. The UTE-MTR values were 15.90%±1.06% and 14.59%±1.35% for normal and mildly degenerated cartilage, respectively. The UTE-AdiabT1ρ values were 40.19±2.87 and 42.6±2.26 ms for normal and mildly degenerated cartilage, respectively. ROC analysis showed that UTE-MTR (AUC =0.805, P=0.001, sensitivity =73.7%, specificity =89.5%) had the highest diagnostic efficacy for mild cartilage degeneration, while UTE-AdiabT1ρ (AUC =0.727, P=0.017) and CubeQuant-T2 (AUC =0.712, P=0.026) showed lower diagnostic efficacy.

CONCLUSIONS

Quantitative UTE-MT and UTE-AdiabT1ρ biomarkers may potentially be used in the evaluation of early cartilage degeneration.

Resilience to height loss of articular cartilage of osteoarthritic stifle joints of old pigs, compared with healthy cartilage from young pigs in a tribological pin-on-plate exposure, revealing similar friction forces

Introduction

We saw a lack of data on the biomechanical behavior of degenerated articular cartilage (OA) compared with that of healthy cartilage, even though the susceptibility to wear and tear of articular cartilage plays a key role in the progression of osteoarthritis (OA). Therefore, we performed a comparison between naturally occurring OA and healthy cartilage from pigs, before and after tribological stress.

Aim

The aim of the study was to compare OA-cartilage with healthy cartilage and to analyze the resilience to tribological shear stress, which will be measured as height loss (HL), and to friction forces of the cartilage layers. The findings will be substantiated in macro- and microscopical evaluations before and after tribological exposure.

Methods

We assessed stifle joints of fifteen old and sixteen young pigs from the local abattoir radiologically, macroscopically and histologically to determine possible OA alterations. We put pins from the femoral part of the joints and plates from the corresponding tibial plateaus in a pin-on-plate tribometer under stress for about two hours with about 1108 reciprocating cycles under a pressure of approximately 1 MPa. As a surrogate criterion of wear and tear, the HL was recorded in the tribometer. The heights of the cartilage layers measured before and after the tribological exposure were compared histologically. The condition of the cartilage before and after the tribological exposure was analyzed both macroscopically with an adapted ICRS score and microscopically according to Little et al. (2010). We assessed the friction forces acting between the surfaces of the cartilage pair–specimens.

Results

Articular cartilage taken from old pigs showed significant degenerative changes compared to that taken from the young animals. The macroscopic and microscopic scores showed strong alterations of the cartilage after the tribological exposure. There was a noticeable HL of the cartilage specimens after the first 100 to 300 cycles. The HL after tribological exposure was lower in the group of the old animals with 0.52 mm ± 0.23 mm than in the group of the young animals with 0.86 mm ± 0.26 mm (p < 0.0001). The data for the HL was validated by the histological height measurements with 0.50 mm ± 0.82 mm for the old and 0.79 mm ±0.53 mm for the young animals (p = 0.133). The friction forces measured at the cartilage of the old animals were 2.25 N ± 1.15 N and 1.89 N ± 1.45 N of the young animals (p = 0.3225).

Conclusion

Unlike articular cartilage from young pigs, articular cartilage from old pigs showed OA alterations. Tribological shear stress exposure revealed that OA cartilage showed less HL than healthy articular cartilage. Tribological stress exposure in a pin–on–plate tribometer seemed to be an appropriate way to analyze the mechanical stability of articular cartilage, and the applied protocol could reveal weaknesses of the assessed cartilage tissue. Friction and HL seemed to be independent parameters when degenerated and healthy articular cartilage were assessed under tribological exposure in a pin–on- plate tribometer.

Functional MRI for evaluation of hyaline cartilage extracelullar matrix, a physiopathological-based approach

MRI of articular cartilage (AC) integrity has potential to become a biomarker for osteoarthritis progression. Traditional MRI sequences evaluate AC morphology, allowing for the measurement of thickness and its change over time. In the last two decades, more advanced, dedicated MRI cartilage sequences have been developed aiming to assess AC matrix composition non-invasively and detect early changes in cartilage not captured on morphological sequences. T2-mapping and T1ρ sequences can be used to estimate the relaxation times of water inside the AC. These sequences have been introduced into clinical protocols and show promising results for cartilage assessment. Extracelullar matrix can also be assessed using diffusion-weighted imaging and diffusion tensor imaging as the movement of water is limited by the presence of extracellular matrix in AC. Specific techniques for glycosaminoglycans (GAG) evaluation, such as delayed gadolinium enhanced MRI of cartilage or Chemical Exchange Saturation Transfer imaging of GAG, as well as sodium imaging have also shown utility in the detection of AC damage. This manuscript provides an educational update on the physical principles behind advanced AC MRI techniques as well as a comprehensive review of the strengths and weaknesses of each approach. Current clinical applications and potential future applications of these techniques are also discussed.

Ex vivo quantitative multiparametric MRI mapping of human meniscus degeneration

OBJECTIVES

To evaluate the diagnostic performance of T1, T1ρ, T2, T2*, and UTE-T2* (ultrashort-echo time-enhanced T2*) mapping in the refined graduation of human meniscus degeneration with histology serving as standard-of-reference.

MATERIALS AND METHODS

This IRB-approved intra-individual comparative ex vivo study was performed on 24 lateral meniscus body samples obtained from 24 patients undergoing total knee replacement. Samples were assessed on a 3.0-T MRI scanner using inversion-recovery (T1), spin-lock multi-gradient-echo (T1ρ), multi-spin-echo (T2) and multi-gradient-echo (T2* and UTE-T2*) sequences to determine relaxation times of quantitative MRI (qMRI) parameters. Relaxation times were calculated on the respective maps, averaged to the entire meniscus and to its zones. Histologically, samples were analyzed on a four-point score according to Williams (0-III). QMRI results and Williams (sub)scores were correlated using Spearman's ρ, while Williams grade-dependent differences were assessed using Kruskal-Wallis and Dunn's tests. Sensitivities and specificities in the detection of intact (Williams grade [WG]-0) and severely degenerate meniscus (WG-II-III) were calculated.

RESULTS

Except for T2*, significant increases in qMRI parameters with increasing Williams grades were observed. T1, T1ρ, T2, and UTE-T2* exhibited high sensitivity and variable specificity rates. Significant marked-to-strong correlations were observed for these parameters with each other, with histological WGs and the subscores tissue integrity and cellularity.

CONCLUSIONS

QMRI mapping holds promise in the objective evaluation of human meniscus. Although sufficient discriminatory power of T1, T1ρ, T2, and UTE-T2* was only demonstrated for the histological extremes, these data may aid in the future MRI-based parameterization and quantification of human meniscus degeneration.

Osteoarthritis in the Knee Joints of Göttingen Minipigs after Resection of the Anterior Cruciate Ligament? Missing Correlation of MRI, Gene and Protein Expression with Histological Scoring

Introduction

The Göttingen Minipig (GM) is used as large animal model in articular cartilage research. The aim of the study was to introduce osteoarthritis (OA) in the GM by resecting the anterior cruciate ligament (ACLR) according to Pond and Nuki, verified by histological and magnetic resonance imaging (MRI) scoring as well as analysis of gene and protein expression.

Materials and Methods

The eight included skeletally mature female GM were assessed after ACLR in the left and a sham operation in the right knee, which served as control. 26 weeks after surgery the knee joints were scanned using a 3-Tesla high-field MR tomography unit with a 3 T CP Large Flex Coil. Standard proton-density weighted fat saturated sequences in coronal and sagittal direction with a slice thickness of 3 mm were used. The MRI scans were assessed by two radiologists according to a modified WORMS-score, the X-rays of the knee joints by two evaluators. Osteochondral plugs with a diameter of 4mm were taken for histological examination from either the main loading zone or the macroscopic most degenerated parts of the tibia plateau or condyle respectively. The histological sections were blinded and scored by three experts according to Little et al. Gene expression analysis was performed from surrounding cartilage. Expression of adamts4, adamts5, acan, col1A1, col2, il-1ß, mmp1, mmp3, mmp13, vegf was determined by qRT-PCR. Immunohistochemical staining (IH) of Col I and II was performed. IH was scored using a 4 point grading (0—no staining; 3-intense staining).

Results and Discussion

Similar signs of OA were evident both in ACLR and sham operated knee joints with the histological scoring result of the ACLR joints with 6.48 ± 5.67 points and the sham joints with 6.86 ± 5.84 points (p = 0.7953) The MRI scoring yielded 0.34 ± 0.89 points for the ACLR and 0.03 ± 0.17 for the sham knee joints. There was no correlation between the histological and MRI scores (r = 0.10021). The gene expression profiles as well as the immunohistochemical findings showed no significant differences between ACLR and sham knee joints. In conclusion, both knee joints showed histological signs of OA after 26 weeks irrespective of whether the ACL was resected or not. As MRI results did not match the histological findings, MRI was obviously unsuitable to diagnose the OA in GM. The analysis of the expression patterns of the 10 genes could not shed light on the question, whether sham operation also induced cartilage erosion or if the degeneration was spontaneous. The modified Pond-Nuki model may be used with reservation in the adult minipig to induce an isolated osteoarthritis.

Multiparametric Classification of Skin from Osteogenesis Imperfecta Patients and Controls by Quantitative Magnetic Resonance Microimaging

The purpose of this study is to evaluate the ability of quantitative magnetic resonance imaging (MRI) to discriminate between skin biopsies from individuals with osteogenesis imperfecta (OI) and skin biopsies from individuals without OI. Skin biopsies from nine controls (unaffected) and nine OI patients were imaged to generate maps of five separate MR parameters, T₁, T₂, k_m, MTR and ADC. Parameter values were calculated over the dermal region and used for univariate and multiparametric classification analysis. A substantial degree of overlap of individual MR parameters was observed between control and OI groups, which limited the sensitivity and specificity of univariate classification. Classification accuracies ranging between 39% and 67% were found depending on the variable of investigation, with T₂ yielding the best accuracy of 67%. When several MR parameters were considered simultaneously in a multivariate analysis, the classification accuracies improved up to 89% for specific combinations, including the combination of T₂ and k_m. These results indicate that multiparametric classification by quantitative MRI is able to detect differences between the skin of OI patients and of unaffected individuals, which motivates further study of quantitative MRI for the clinical diagnosis of OI.

Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration

OBJECTIVE

To evaluate the usefulness of quantitative parameters obtained by optical coherence tomography (OCT) and magnetic resonance imaging (MRI) in the comprehensive assessment of human articular cartilage degeneration.

MATERIALS AND METHODS

Human osteochondral samples of variable degeneration (n = 45) were obtained from total knee replacements and assessed by MRI sequences measuring T1, T1ρ, T2 and T2* relaxivity and by OCT-based quantification of irregularity (OII, optical irregularity index), homogeneity (OHI, optical homogeneity index]) and attenuation (OAI, optical attenuation index]). Samples were also assessed macroscopically (Outerbridge classification) and histologically (Mankin classification) as grade-0 (Mankin scores 0-4)/grade-I (scores 5-8)/grade-II (scores 9-10)/grade-III (score 11-14). After data normalisation, differences between Mankin grades and correlations between imaging parameters were assessed using ANOVA and Tukey's post-hoc test and Spearman's correlation coefficients, respectively. Sensitivities and specificities in the detection of Mankin grade-0 were calculated.

RESULTS

Significant degeneration-related increases were found for T2 and OII and decreases for OAI, while T1, T1ρ, T2* or OHI did not reveal significant changes in relation to degeneration. A number of significant correlations between imaging parameters and histological (sub)scores were found, in particular for T2 and OII. Sensitivities and specificities in the detection of Mankin grade-0 were highest for OHI/T1 and OII/T1ρ, respectively.

CONCLUSION

Quantitative OCT and MRI techniques seem to complement each other in the comprehensive assessment of cartilage degeneration. Sufficiently large structural and compositional changes in the extracellular matrix may thus be parameterized and quantified, while the detection of early degeneration remains challenging.

Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

OBJECTIVE

The purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions.

METHOD

An approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the Osteoarthritis Research Society International (OARSI) histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug.

RESULTS

The binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values.

CONCLUSION

MRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis.