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: The knee joint is one of the most common sites for osteoarthritis, the onset and progression of which are believed to relate to the mechanical environment of cartilage. To understand this environment, it is necessary to take into account the complex biphasic contact interactions of the cartilage and menisci. In this study, the time-dependent contact behaviour of an intact and a meniscectomized human tibiofemoral joint was characterized under body weight using a computational model. Good agreement in the contact area and femoral displacement under static loads were found between model predictions of this study and published experimental measurements. The time-dependent results indicated that as loading time progressed, the contact area and femoral vertical displacement of both intact and meniscectomized joints increased. More load was transferred to the cartilage-cartilage interface over time. However, the portions of load borne by the lateral and medial compartments did not greatly vary with time. Additionally, during the whole simulation period, the maximum compressive stress in the meniscectomized joint was higher than that in the intact joint. The fluid pressure in the intact and meniscectomized joints remained remarkably high at the condyle centres, but the fluid pressure at the cartilage-meniscus interface decreased faster than that at the condyle centres as loading time progressed. The above findings provide further insights into the mechanical environment of the cartilage and meniscus within the human knee joint. © IMechE 2014.Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine 11/2014; 228(11):1193 – 1207. DOI:10.1177/0954411914559737 · 1.14 Impact Factor
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ABSTRACT: The pathogenesis of osteoarthritis (OA) includes complex events in the whole joint. Cartilage loss and bone remodelling are central in OA progression. In this project, we investigated the feasibility of quantifying OA by analysis of the tibial trabecular bone structure in low-field knee magnetic resonance imaging (MRI). The development of automatic and more sensitive indicators of OA in conjunction with low cost equipment have the potential to decrease the length and cost of clinical trials. We present a texture analysis methodology that combined uncommitted machine-learning techniques in a fully automatic framework. Different linear feature selection approaches where investigated. The methodology was evaluated in a longitudinal study, where MRI scans of knees were used to quantify the tibial trabecular bone in a bone marker for OA diagnosis and another marker for prediction of tibial cartilage loss. The healthy and diseased subjects were defined by the Kellgren and Lawrence index assigned by radiologists and the levels of cartilage loss were assessed by a segmentation process. A preliminary radiological reading of the knees with high and low risks of cartilage loss suggested the prognosis bone marker captured aspects of the vertical trabecularization of the tibial bone to define the prognosis of cartilage loss. We also investigated which region of the tibia provided the best prognosis for medial tibial cartilage loss. The structure of the tibial trabecular bone was divided in localized subregions in an attempt to capture the different pathological features occurring at each location. We applied multiple-instance learning, where each subregion was defined to be one instance and a bag held all instances over a full region-of-interest. The inferior part of the tibial bone was classified as the most relevant region for prognosis of cartilage loss and a preliminary radiological reading of a subset of the samples suggested the bone marker also captured the vertical trabecularization of the tibial bone to define the most relevant region. In a clinical point of view, besides presenting a bone marker able to predict disease progression and diagnostic bone marker superior to other OA biomarkers, our findings underlined the importance of the trabecular bone to the understanding of the OA pathology.04/2013, Degree: PhD, Supervisor: Mads Nielsen and Erik B. Dam
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ABSTRACT: This study aims to assess mean signal intensity of cartilage on T1-weighted magnetic resonance imaging (MRI) images, and then examine whether mean signal intensity is associated with risk factors and measures of osteoarthritis in younger and older adults. A total of 50 younger adult subjects (mean age 41, range 29-57; 64 % female; baseline only) and 168 older adult subjects (mean age 63, range 52-78; 46 % female; baseline and 2.9 year followup) were randomly selected from the community. T1-weighted fat-supressed gradient recall echo MRI scans of right knees were performed. Image segmentation was performed semi-automatically, and measures of mean signal intensity and cartilage thickness for regions of cartilage were obtained. Urinary levels of C-terminal crosslinking telopeptide of type II collagen (U-CTX-II) were measured in younger adults. Cartilage defects were scored using a 5-point scale in both groups. In multivariable analyses, higher cartilage defects and BMI were significantly associated with lower same-region mean signal intensity in younger and older adults. CTX-II was negatively and significantly associated with mean signal intensity of cartilage in the lateral femoral and patellar sites. Joint space narrowing and osteophytes analysed in older adults were significantly associated with reduced mean signal intensity at various sites. Over 2.9 years, lower mean signal intensity at femoral and patellar sites and in whole knee was associated with decreases in cartilage thickness. Reduced mean signal intensity of cartilage on T1-weighted gradient recall echo MRI is associated with osteoarthritis risk factors and predicts cartilage loss suggesting low cartilage signal intensity may reflect early osteoarthritic changes.Clinical Rheumatology 12/2013; 33(3). DOI:10.1007/s10067-013-2447-4 · 1.77 Impact Factor