Abstract
Osteoarthritis (OA) is a widespread, chronic joint disease for which there are currently no effective treatments beyond symptom relief. The lack of any approved disease modifying osteoarthritic drugs may partly be explained by insufficient disease understanding, but may also be tied to the absence of sensitive biomarkers for monitoring disease progression. This thesis investigates how subregional measures of cartilage thickness can be used to improve upon current imaging biomarkers.
The first part of this investigation aims to discover discriminative areas in the cartilage using machine-learning techniques specifically developed to take advantage of the spatial nature of the problem. The methods were evaluated on data from a longitudinal study where detailed cartilage thickness maps were quantified from magnetic resonance images. The results showed that focal differences in cartilage thickness may be relevant for both OA diagnosis and for prediction of future cartilage loss.
The second part of the thesis investigates spatial patterns of longitudinal cartilage thickness changes in healthy and OA knees. Based on our findings, we propose a new, conceptually simple biomarker that embraces the heterogeneous spatial cartilage changes that were observed in our study and in recent literature. The cartilage “Activity” marker is shown to have a state-of-the-art performance in separating healthy knees from OA knees and is also shown to predict knee replacement which is a clinically relevant endpoint for OA.
The first part of this investigation aims to discover discriminative areas in the cartilage using machine-learning techniques specifically developed to take advantage of the spatial nature of the problem. The methods were evaluated on data from a longitudinal study where detailed cartilage thickness maps were quantified from magnetic resonance images. The results showed that focal differences in cartilage thickness may be relevant for both OA diagnosis and for prediction of future cartilage loss.
The second part of the thesis investigates spatial patterns of longitudinal cartilage thickness changes in healthy and OA knees. Based on our findings, we propose a new, conceptually simple biomarker that embraces the heterogeneous spatial cartilage changes that were observed in our study and in recent literature. The cartilage “Activity” marker is shown to have a state-of-the-art performance in separating healthy knees from OA knees and is also shown to predict knee replacement which is a clinically relevant endpoint for OA.
| Original language | English |
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| Publisher | Department of Computer Science, Faculty of Science, University of Copenhagen |
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| Number of pages | 115 |
| Publication status | Published - 2013 |