Automatic morphometric cartilage quantification in the medial tibial plateau from MRI for osteoarthritis grading
ABSTRACT To evaluate whether a novel, fully automatic, morphometric cartilage quantification framework is suitable for assessing level of knee osteoarthritis (OA) in clinical trials.
The population was designed with a normal population and groups with varying degree of OA of both sexes and at ages from 21 to 78. Posterior-anterior X-rays were acquired in semi-flexed, load-bearing position. The radiographic signs of OA were evaluated based on the Kellgren and Lawrence score (KL) and the joint space width (JSW) was measured. Turbo 3D T1 magnetic resonance imaging (MRI) scans were acquired with resolution 0.7x0.7x0.8mm(3) from a 0.18T scanner. The morphometric cartilage quantification from MRI resulted in volume, surface area, thickness and surface curvature for the medial tibial cartilage compartment. These quantifications were evaluated against JSW with respect to precision and ability to separate healthy subjects from OA subjects.
The automatic, morphometric cartilage quantifications allowed fairly precise measurements with scan-rescan coefficient of variations (CVs) in the range from 3.4% to 6.3%. All quantifications, including JSW, allowed separation of the groups of healthy and OA subjects. However, for separation of the healthy from the borderline cases (KL 0 vs KL 1), only the Cartilage Curvature quantification allowed statistically significant separation (P<0.01).
The novel morphometric framework shows promise for use in clinical trials. The ability of the Cartilage Curvature quantification to detect the early stages of OA and the effectiveness of the focal thickness Q10 measure are particularly noteworthy. Furthermore, these results may indirectly support that low-field MRI may be a low-cost option for clinical trials.
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ABSTRACT: Osteoarthritis (OA) is a heterogeneous disorder, with several possible drivers of disease progression. Since up to 50% of OA patients in clinical studies and approximately 85% in the background population do not progress, it is important to develop means of identifying fast progressors. The aim of this study was to identify key characteristics of disease progression through investigation of the association of radiographic progression over two years with baseline Joint Space Width (JSW), Kellgren Lawrence (KL) grade, WOMAC pain, Joint Space Narrowing (JSN), and BMI.Osteoarthritis and Cartilage 01/2015; 23(4). DOI:10.1016/j.joca.2014.12.024 · 4.66 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
- Principles of Osteoarthritis- Its Definition, Character, Derivation and Modality-Related Recognition, 02/2012; , ISBN: 978-953-51-0063-8