Osteoarthritic cartilage is more homogeneous than healthy cartilage: identification of a superior region of interest colocalized with a major risk factor for osteoarthritis.
ABSTRACT Cartilage loss as determined by magnetic resonance imaging (MRI) or joint space narrowing as determined by x-ray is the result of cartilage erosion. However, metabolic processes within the cartilage that later result in cartilage loss may be a more sensitive assessment method for early changes. Recently, it was shown that cartilage homogeneity visualized by MRI representing the biochemical changes undergoing in the cartilage is a potential marker for early detection of knee osteoarthritis (OA) and is also able to significantly separate groups of healthy subjects from those with OA. The purpose of this study was twofold. First, we wished to evaluate whether the results on cartilage homogeneity from the previous study can be reproduced using an independent population. Second, based on the homogeneity framework, we present an automatic technique that partitions the region of interest in the cartilage that contributes most to discrimination between healthy and OA subjects and allows for identification of the most implicated areas in early OA. These findings may allow further investigation of whether cartilage homogeneity reveals a predisposition for OA or whether it evolves as a consequence to disease and thereby can be used as a progression biomarker.
A total of 283 right and left knees from 159 subjects aged 21 to 81 years were scanned using a Turbo 3D T1 sequence on a 0.18-T MRI Esaote scanner. The medial compartment of the tibial cartilage sheet was segmented using a fully automatic voxel classification scheme based on supervised learning. From the segmented cartilage sheet, homogeneity was quantified by measuring entropy from the distribution of signal intensities inside the compartment. Each knee was examined by radiography, and the knees were categorized by the Kellgren and Lawrence (KL) Index. Next, based on a gradient descent optimization technique, the cartilage region that contributed to the maximum statistical significance of homogeneity in separating healthy subjects from the diseased was partitioned. The generalizability of the region was evaluated by testing for overfitting. Three different regularization techniques were evaluated for reducing overfitting errors.
The P values for separating the different groups based on cartilage homogeneity were 2 x 10(-5) (KL 0 versus KL 1) and 1 x 10(-7) (KL 0 versus KL >0). Using the automatic gradient descent technique, the partitioned region was toward the peripheral part of the cartilage sheet. Using this region, the P values for separating the different groups based on homogeneity were 5 x 10(-9) (KL 0 versus KL 1) and 1 x 10(-15) (KL 0 versus KL >0). The precision of homogeneity for the partitioned region assessed as a test-retest root-mean-square coefficient of variation was 3.3%. Bootstrapping proved to be an effective regularization tool in reducing overfitting errors.
The validation study supported the use of cartilage homogeneity as a tool for the early detection of knee OA and for separating groups of healthy subjects from those who have disease. Our automatic, unbiased partitioning algorithm based on a general statistical framework outlined the cartilage region of interest that best separated healthy from OA conditions on the basis of homogeneity discrimination. We have shown that OA affects certain areas of the cartilage more distinctly, and these areas are located more toward the peripheral region of the cartilage. We propose that this region corresponds anatomically to cartilage covered by the meniscus in healthy subjects. This finding may provide valuable clues in the early detection and monitoring of OA and thus may improve treatment efficacy.
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ABSTRACT: Whole organ magnetic resonance imaging (MRI)-based semiquantitative (SQ) assessment of knee osteoarthritis (OA), based on reliable scoring methods and expert reading, has become a powerful research tool in OA. SQ morphologic scoring has been applied to large observational cross-sectional and longitudinal epidemiologic studies as well as interventional clinical trials. SQ whole organ scoring analyzes all joint structures that are potentially relevant as surrogate outcome measures of OA and potential disease modification, including cartilage, subchondral bone, osteophytes, intra- and periarticular ligaments, menisci, synovial lining, cysts, and bursae. Resources needed for SQ scoring rely on the MRI protocol, image quality, experience of the expert readers, method of documentation, and the individual scoring system that will be applied. The first part of this article discusses the different available OA whole organ scoring systems, focusing on MRI of the knee, and also reviews alternative approaches. Rheumatologists are made aware of artifacts and differential diagnoses when applying any of the SQ scoring systems. The second part focuses on quantitative approaches in OA, particularly measurement of (subregional) cartilage loss. This approach allows one to determine minute changes that occur relatively homogeneously across cartilage structures and that are not apparent to the naked eye. To this end, the cartilage surfaces need to be segmented by trained users using specialized software. Measurements of knee cartilage loss based on water-excitation spoiled gradient recalled echo acquisition in the steady state, fast low-angle shot, or double-echo steady-state imaging sequences reported a 1% to 2% decrease in cartilage thickness annually, and a high degree of spatial heterogeneity of cartilage thickness changes in femorotibial subregions between subjects. Risk factors identified by quantitative measurement technology included a high body mass index, meniscal extrusion and meniscal tears, knee malalignment, advanced radiographic OA grade, bone marrow alterations, and focal cartilage lesions.Rheumatic diseases clinics of North America 08/2009; 35(3):521-55. DOI:10.1016/j.rdc.2009.08.006 · 1.74 Impact Factor
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ABSTRACT: We propose a fully automatic statistical framework for identifying the non-negative, real-valued weight map that best discriminate between two groups of objects. Given measurements on a spatially defined grid, a numerical optimization scheme is used to find the weight map that minimizes the sample size required to discriminate the two groups. The weight map produced by the method reflects the relative importance of the different areas in the objects, and the resulting sample size reduction is an important end goal in situations where data collection is difficult or expensive. An example is in clinical studies where the cost and the patient burden are directly related to the number of participants needed for the study. In addition, inspection of the weight map might provide clues that can lead to a better clinical understanding of the objects and pathologies being studied. The method is evaluated on synthetic data and on clinical data from knee cartilage MRI. The clinical data contain a total of 159 subjects aged 21-81 years and ranked from zero to four on the Kellgren-Lawrence osteoarthritis severity scale. Compared to a uniform weight map, we achieve sample size reductions up to 58% for cartilage thickness measurements. Based on quantifications from both morphometric and textural based imaging features, we also identify the most pathological areas in the articular cartilage.Medical image analysis 02/2010; 14(3):255-64. DOI:10.1016/j.media.2010.01.004 · 3.68 Impact Factor