Journal
FRONTIERS IN ENDOCRINOLOGY
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fendo.2021.778537
Keywords
bone mineral density; convolutional neural network; opportunistic imaging; osteoporosis; proton density fat fraction; texture analysis; vertebral fracture
Categories
Funding
- European Research Council [677661, 637164]
- German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) [432290010]
- B. Braun Foundation [BBST-D-19-00106]
- German Academic Exchange Service (Deutscher Akademischer Austauschdienst, DAAD)
- German Society of Musculoskeletal Radiology (Deutsche Gesellschaft fur Muskuloskelettale Radiologie, DGMSR)
- European Research Council (ERC) [637164] Funding Source: European Research Council (ERC)
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The study demonstrates that texture analysis combined with CT and CSE-MRI data can effectively differentiate between patients with and without osteoporotic vertebral fractures. A model incorporating T2* and PDFF-based texture features shows promise in explaining the variance of integral vBMD, with a better performance in differentiating patients with fractures compared to models based solely on integral vBMD and PDFF.
Purpose: Osteoporosis is a highly prevalent skeletal disease that frequently entails vertebral fractures. Areal bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the reference standard, but has well-known limitations. Texture analysis can provide surrogate markers of tissue microstructure based on computed tomography (CT) or magnetic resonance imaging (MRI) data of the spine, thus potentially improving fracture risk estimation beyond areal BMD. However, it is largely unknown whether MRI-derived texture analysis can predict volumetric BMD (vBMD), or whether a model incorporating texture analysis based on CT and MRI may be capable of differentiating between patients with and without osteoporotic vertebral fractures.Materials and Methods: Twenty-six patients (15 females, median age: 73 years, 11 patients showing at least one osteoporotic vertebral fracture) who had CT and 3-Tesla chemical shift encoding-based water-fat MRI (CSE-MRI) available were analyzed. In total, 171 vertebral bodies of the thoracolumbar spine were segmented using an automatic convolutional neural network (CNN)-based framework, followed by extraction of integral and trabecular vBMD using CT data. For CSE-MRI, manual segmentation of vertebral bodies and consecutive extraction of the mean proton density fat fraction (PDFF) and T2* was performed. First-order, second-order, and higher-order texture features were derived from texture analysis using CT and CSE-MRI data. Stepwise multivariate linear regression models were computed using integral vBMD and fracture status as dependent variables.Results: Patients with osteoporotic vertebral fractures showed significantly lower integral and trabecular vBMD when compared to patients without fractures (p < 0.001). For the model with integral vBMD as the dependent variable, T2* combined with three PDFF-based texture features explained 40% of the variance (adjusted R-2 [R-a(2) ] = 0.40; p < 0.001). Furthermore, regarding the differentiation between patients with and without osteoporotic vertebral fractures, a model including texture features from CT and CSE-MRI data showed better performance than a model based on integral vBMD and PDFF only (R-a(2) = 0.47 vs. R-a(2) = 0.81; included texture features in the final model: integral vBMD, CT_Short-run_emphasis, CT_Varianceglobal, and PDFF_Variance).Conclusion: Using texture analysis for spine CT and CSE-MRI can facilitate the differentiation between patients with and without osteoporotic vertebral fractures, implicating that future fracture prediction in osteoporosis may be improved.
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