Bone density and texture from minimally post-processed knee radiographs in subjects with knee osteoarthritis
|Author:||Hirvasniemi, Jukka1; Niinimäki, Jaakko2,3; Thevenot, Jérôme3;|
1Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
3Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019060618830
|Publish Date:|| 2019-02-14
Plain radiography is the most common modality to assess the stage of osteoarthritis. Our aims were to assess the relationship of radiography-based bone density and texture between radiographs with minimal and clinical post-processing, and to compare the differences in bone characteristics between controls and subjects with knee osteoarthritis or medial tibial bone marrow lesions (BMLs). Tibial bone density and texture was evaluated from radiographs with both minimal and clinical post-processing in 109 subjects with and without osteoarthritis. Bone texture was evaluated using fractal signature analysis. Significant correlations (p < 0.001) were found in all regions (between 0.94 and 0.97) for calibrated bone density between radiographs with minimal and clinical post-processing. Correlations varied between 0.51 and 0.97 (p <0.001) for FDVer texture parameter and between −0.10 and 0.97 for FDHor. Bone density and texture were different (p < 0.05) between controls and subjects with osteoarthritis or BMLs mainly in medial tibial regions. When classifying healthy and osteoarthritic subjects using a machine learning-based elastic net model with bone characteristics, area under the receiver operating characteristics (ROCAUC) curve was 0.77. For classifying controls and subjects with BMLs, ROCAUC was 0.85. In conclusion, differences in bone density and texture can be assessed from knee radiographs when using minimal post-processing.
Annals of biomedical engineering
|Pages:||1181 - 1190|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
Open access funding provided by University of Oulu including Oulu University Hospital. The research leading to these results has received funding from the Academy of Finland (Projects 268378 and 308165), European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement No. 336267, and Business Finland: Finnish Funding Agency for Innovation (Grant Number 1241/31/2016). The funding sources had no role in the study design, data collection or analysis, interpretation of data, writing of the manuscript, or in the decision to submit the manuscript for publication.
|Academy of Finland Grant Number:||
268378 (Academy of Finland Funding decision)
308165 (Academy of Finland Funding decision)
© The Author(s) 2019
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