Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures

A. Valentinitsch, S. Trebeschi, J. Kaesmacher, C. Lorenz, M. T. Löffler, C. Zimmer, T. Baum, J. S. Kirschke

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

Summary: Our study proposed an automatic pipeline for opportunistic osteoporosis screening using 3D texture features and regional vBMD using multi-detector CT images. A combination of different local and global texture features outperformed the global vBMD and showed high discriminative power to identify patients with vertebral fractures. Introduction: Many patients at risk for osteoporosis undergo computed tomography (CT) scans, usable for opportunistic (non-dedicated) screening. We compared the performance of global volumetric bone mineral density (vBMD) with a random forest classifier based on regional vBMD and 3D texture features to separate patients with and without osteoporotic fractures. Methods: In total, 154 patients (mean age 64 ± 8.5, male; n = 103) were included in this retrospective single-center analysis, who underwent contrast-enhanced CT for other reasons than osteoporosis screening. Patients were dichotomized regarding prevalent vertebral osteoporotic fractures (noFX, n = 101; FX, n = 53). Vertebral bodies were automatically segmented, and trabecular vBMD was calculated with a dedicated phantom. For 3D texture analysis, we extracted gray-level co-occurrence matrix Haralick features (HAR), histogram of gradients (HoG), local binary patterns (LBP), and wavelets (WL). Fractured vertebrae were excluded for texture-feature and vBMD data extraction. The performance to identify patients with prevalent osteoporotic vertebral fractures was evaluated in a fourfold cross-validation. Results: The random forest classifier showed a high discriminatory power (AUC = 0.88). Parameters of all vertebral levels significantly contributed to this classification. Importantly, the AUC of the proposed algorithm was significantly higher than that of volumetric global BMD alone (AUC = 0.64). Conclusion: The presented classifier combining 3D texture features and regional vBMD including the complete thoracolumbar spine showed high discriminatory power to identify patients with vertebral fractures and had a better diagnostic performance than vBMD alone.

Original languageEnglish
Pages (from-to)1275-1285
Number of pages11
JournalOsteoporosis International
Volume30
Issue number6
DOIs
StatePublished - 1 Jun 2019

Keywords

  • BMD
  • Machine learning
  • Opportunistic screening
  • Osteoporosis
  • Quantitative computed tomography
  • Random forest model
  • Texture analysis
  • Vertebral fractures

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