Erik B. Dam

Should subchondral bone turnover be targeted when treating osteoarthritis

OBJECTIVE Osteoarthritis (OA) is the most common form of arthritic disease, and it is a major cause of disability and impaired quality of life in the elderly. OA is a complex disease of the entire joint, including bone and cartilage, thereby presenting alternative approaches for treatment. This review summarizes emerging observations from cell biology to preliminary clinical trials, describing interactions between the bone and cartilage components. We speculate whether a treatment for OA would be possible without targeting the bone compartment? METHODS Peer-reviewed articles found using pre-defined search criteria and published in the PubMed database until June 2007 are summarized. In addition, abstracts from the OsteoArthritis Research Society International (OARSI) conferences in the time period 2000-2007 were included. RESULTS Bone and cartilage health seem to be tightly associated. Ample evidence is found for bone changes during progression of OA, including, but not limited to, increased turnover in the subchondral bone, thinning of the trabecular structure, osteophytes, bone marrow lesions and sclerosis of the subchondral plate. In addition, a range of investigations has described secondary positive effects on cartilage health when bone resorption was suppressed, or deterioration of the cartilage when resorption is increased. CONCLUSION An optimal treatment for OA might include targeting both the bone and cartilage compartments. Hence, as several cell systems are to be targeted in a safe manner, limited options seem possible.

Segmenting Articular Cartilage Automatically Using a Voxel Classification Approach

We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual and automated image analysis techniques, low-field scanners on the other hand produce lower quality images but to a fraction of the cost of their high-field counterpart. For low-field MRI, there is no well-established accuracy validation for quantitative cartilage estimates, but we show that differences between healthy and osteoarthritic populations are statistically significant using our cartilage volume and surface area estimates, which suggests that low-field MRI analysis can become a useful, affordable tool in clinical studies

Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively. We applied our method to the segmentation of tibial cartilage in low field knee MRI scans and tested it on 114 unseen scans. Although our method uses only 2D features at a single scale, it performs better than a state-of-the-art method using 3D multi-scale features. In the latter approach, the features and the classifier have been carefully adapted to the problem at hand. That we were able to get better results by a deep learning architecture that autonomously learns the features from the images is the main insight of this study.

The disease modifying osteoarthritis drug (DMOAD): Is it in the horizon?

Till date, the pharmaceutical industry has failed to bring effective and safe disease modifying osteoarthritic drugs (DMOADs) to the millions of patients suffering from this serious and deliberating disease. We provide a review of recent data reported on the investigation of DMOADs in clinical trials, including compounds inhibiting matrix-metalloproteinases (MMPs), bisphosphonates, cytokine blockers, calcitonin, inhibitors of inducible nitric oxide synthase (iNOS), doxycycline, glucosamine, and diacereine. We discuss the challenges associated with the drug development process in general and with DMOADs in particular, and we advance the need for a new development paradigm for DMOADs. Two central elements in this paradigm are a stronger focus on the biology of the joint and the application of new and more sensitive biomarkers allowing redesign of clinical trials in osteoarthritis.

Identification of progressors in osteoarthritis by combining biochemical and MRI-based markers

IntroductionAt present, no disease-modifying osteoarthritis drugs (DMOADS) are approved by the FDA (US Food and Drug Administration); possibly partly due to inadequate trial design since efficacy demonstration requires disease progression in the placebo group. We investigated whether combinations of biochemical and magnetic resonance imaging (MRI)-based markers provided effective diagnostic and prognostic tools for identifying subjects with high risk of progression. Specifically, we investigated aggregate cartilage longevity markers combining markers of breakdown, quantity, and quality.MethodsThe study included healthy individuals and subjects with radiographic osteoarthritis. In total, 159 subjects (48% female, age 56.0 ± 15.9 years, body mass index 26.1 ± 4.2 kg/m2) were recruited. At baseline and after 21 months, biochemical (urinary collagen type II C-telopeptide fragment, CTX-II) and MRI-based markers were quantified. MRI markers included cartilage volume, thickness, area, roughness, homogeneity, and curvature in the medial tibio-femoral compartment. Joint space width was measured from radiographs and at 21 months to assess progression of joint damage.ResultsCartilage roughness had the highest diagnostic accuracy quantified as the area under the receiver-operator characteristics curve (AUC) of 0.80 (95% confidence interval: 0.69 to 0.91) among the individual markers (higher than all others, P < 0.05) to distinguish subjects with radiographic osteoarthritis from healthy controls. Diagnostically, cartilage longevity scored AUC 0.84 (0.77 to 0.92, higher than roughness: P = 0.03). For prediction of longitudinal radiographic progression based on baseline marker values, the individual prognostic marker with highest AUC was homogeneity at 0.71 (0.56 to 0.81). Prognostically, cartilage longevity scored AUC 0.77 (0.62 to 0.90, borderline higher than homogeneity: P = 0.12). When comparing patients in the highest quartile for the longevity score to lowest quartile, the odds ratio of progression was 20.0 (95% confidence interval: 6.4 to 62.1).ConclusionsCombination of biochemical and MRI-based biomarkers improved diagnosis and prognosis of knee osteoarthritis and may be useful to select high-risk patients for inclusion in DMOAD clinical trials.

Enzyme-linked immunosorbent assay (ELISAs) for metalloproteinase derived type II collagen neoepitope, CIIM—Increased serum CIIM in subjects with severe radiographic osteoarthritis

OBJECTIVES In joint degenerative diseases, the collagens are degraded by matrix metalloproteinases and protein fragments are released to serum as potential biomarkers. METHODS A collagen type II specific neoepitope, CIIM, was identified (…RDGAAG(1053)) by mass spectrometry. Two ELISAs against the neoepitope were developed. CIIM was measured in cartilage explants in the presence or absence of protease inhibitors. CIIM was measured in OA synovial fluid (n=51) and serum (n=156). Knee OA was graded by standard Kellgren-Lawrence (KL) score. RESULTS The ELISAs showed good technical performance; CV%, <13%. CIIM release from cartilage explants was blocked by the MMP inhibitor. CIIM was detected in synovial fluid. Furthermore, serum CIIM levels were significantly higher (P<0.05) in those individuals with mild or severe OA than in those with no OA. CONCLUSION We developed a new biomarker for joint degenerative diseases, which we demonstrated was derived from MMP-degraded type II collagen.

Which elements are involved in reversible and irreversible cartilage degradation in osteoarthritis

Osteoarthritis (OA) is a disease of the entire joint. Different treatment strategies for OA have been proposed and tested clinically without the desired efficacy. One reason for the scarcity of current chondroprotective agents may be the insufficient understanding of the patho-physiology of the joint and whether the joint damage is reversible or irreversible. In this review, we compile emerging data on cellular and pathological aspects of OA, and ask whether these data could give clue to when cartilage degradation is reversible and whether a point-of-no-return exists. We highlight different stages of OA, and speculate whether different intervention strategies (e.g. DMOAD vs. SMOADs) may only be efficacious at distinct stages of OA.

Increased urinary excretion of C-telopeptides of type II collagen (CTX-II) predicts cartilage loss over 21 months by MRI

OBJECTIVE Osteoarthritis (OA) is characterized by increased bone and cartilage metabolism leading to joint damage. The urinary excretion of C-telopeptides of type II collagen (CTX-II) has earlier predicted progression in radiographic OA (ROA)--useful for participant selection in clinical studies of potential disease modifying OA drugs (DMOADs). We investigated the longitudinal interrelationship between CTX-II and knee cartilage volume quantified from magnetic resonance imaging (MRI). METHODS We followed 158 subjects [48% females, 36 with knee ROA at baseline (BL)] for 21 months. The Kellgren and Lawrence (KL) index and joint space width were assessed from radiographs (acquired load-bearing, semi-flexed). MRI scans were acquired from a 0.18 T Esaote scanner (40 degrees flip angle (FA), TR 50 ms, TE 16 ms, scan time 10 min, resolution 0.7 mm x 0.7 mm x 0.8 mm) and medial tibial and femoral cartilage volume was quantified. Radiographs and MRI were acquired at BL and follow-up. Fasting morning urine samples (second void) were collected for BL CTX-II measurement. RESULTS CTX-II was 56% higher in ROA subjects (P=0.0001). In addition, elevated BL CTX-II was associated with radiographic progression (by KL or joint space narrowing) although not statistically significant. Contrarily, elevated BL CTX-II predicted longitudinal cartilage loss by MRI (middle/high tertiles had odds ratios 4.0/3.9, P<0.01) corresponding to 3.1% increased yearly cartilage loss. CONCLUSION Prognostic markers in study selection criteria must ensure that placebo-treated participants progress to enable efficacy demonstration. And efficacy markers must allow progression detection within the study period. Our results support applying CTX-II for selection of high risk subjects and applying the fully automatic MRI-based framework for quantification of cartilage loss.

Automatic segmentation of the articular cartilage in knee MRI using a hierarchical multi-class classification scheme

Osteoarthritis is characterized by the degeneration of the articular cartilage in joints. We have developed a fully automatic method for segmenting the articular cartilage in knee MR scans based on supervised learning. A binary approximate kNN classifier first roughly separates cartilage from background voxels, then a three-class classifier assigns one of three classes to each voxel that is classified as cartilage by the binary classifier. The resulting sensitivity and specificity are 90.0% and 99.8% respectively for the medial cartilage compartments. We show that an accurate automatic cartilage segmentation is achievable using a low-field MR scanner.

Automatic quantification of local and global articular cartilage surface curvature: Biomarkers for osteoarthritis?

The objective of this study was to quantitatively assess the surface curvature of the articular cartilage from low‐field magnetic resonance imaging (MRI) data, and to investigate its role in populations with varying radiographic signs of osteoarthritis (OA), cross‐sectionally and longitudinally. The curvature of the articular surface of the medial tibial compartment was estimated both on fine and coarse scales using two different automatic methods which are both developed from an automatic 3D segmentation algorithm. Cross‐sectionally (n = 288), the surface curvature for both the fine‐ and coarse‐scale estimates were significantly higher in the OA population compared with the healthy population, with P < 0.001 and P ≪ 0.001, respectively. For the longitudinal study (n = 245), there was a significant increase in fine‐scale curvature for healthy and borderline OA populations (P < 0.001), and in coarse‐scale curvature for severe OA populations (P < 0.05). Fine‐scale curvature could predict progressors using the estimates of those healthy at baseline (P < 0.001). The inter‐scan precision was 2.2 and 6.5 (mean CV) for the fine‐ and coarse scale curvature measures, respectively. The results showed that quantitative curvature estimates from low‐field MRI at different scales could potentially become biomarkers targeted at different stages of OA. Magn Reson Med 59:1340–1346, 2008.