Automatic quantification of local and global articular cartilage surface curvature: Biomarkers for osteoarthritis?
Department of Computer Science, University of Copenhagen, Universitetsparken 1, DK-2100 Copenhagen E, Denmark. Magnetic Resonance in Medicine
(Impact Factor: 3.57).
06/2008; 59(6):1340-6. DOI: 10.1002/mrm.21560
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.
Available from: PubMed Central
- "Quantitative measures of surface curvature and joint incongruity have also been determined from MR images  and were observed to discriminate between subjects with various radiographic OA grades cross-sectionally at 0.2 T [38, 39]. Curvature estimates at different scales (at 0.2 T) were reported to be associated with the magnitude of cartilage loss longitudinally  and cartilage homogeneity (quantified by measuring entropy from the distribution of signal intensities in tibial cartilage from 0.2 T gradient echo images) was reported to discriminate between subjects without and with early radiographic OA . This measure was proposed to be particularly sensitive in peripheral regions, where the cartilage is covered by the meniscus . "
[Show abstract] [Hide abstract]
ABSTRACT: Quantitative measures of cartilage morphology (i.e., thickness) represent potentially powerful surrogate endpoints in osteoarthritis (OA). These can be used to identify risk factors of structural disease progression and can facilitate the clinical efficacy testing of structure modifying drugs in OA. This paper focuses on quantitative imaging of articular cartilage morphology in the knee, and will specifically deal with different cartilage morphology outcome variables and regions of interest, the relative performance and relationship between cartilage morphology measures, reference values for MRI-based knee cartilage morphometry, imaging protocols for measurement of cartilage morphology (including those used in the Osteoarthritis Initiative), sensitivity to change observed in knee OA, spatial patterns of cartilage loss as derived by subregional analysis, comparison of MRI changes with radiographic changes, risk factors of MRI-based cartilage loss in knee OA, the correlation of MRI-based cartilage loss with clinical outcomes, treatment response in knee OA, and future directions of the field.
Available from: Jenny Folkesson
- "Several semi-automatic methods for cartilage quantification have been reported [17-19], including scoring systems integrating several joint features – for example, the Whole-Organ Magnetic Resonance Imaging Score . Our group recently reported a fully automatic computer-based framework for quantification of several morphometric parameters, including cartilage volume, thickness, homogeneity, and curvature [21-24], targeting both cartilage quantity and quality. "
[Show abstract] [Hide abstract]
ABSTRACT: At 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.
The 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.
Cartilage 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).
Combination 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.
Available from: Martin Styner
[Show abstract] [Hide abstract]
ABSTRACT: In this paper, we present an evaluation framework for the 3D segmentation of knee bones and cartilage from magnetic resonance im-ages. The framework was established for one of the three challenges at the "Medical Image Analysis for the Clinic: A Grand Challenge" workshop held at the 2010 Medical Image Computing and Computer Assisted Inter-vention (MICCAI) conference in Beijing, China. After this workshop, the framework will remain open to online submissions via www.ski10.org. We describe the motivation for this challenge, the preparation of training and test datasets, and the evaluation measures used to rate submitted results.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.