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

• Published in: Osteoporosis international Vol. 30; no. 6; pp. 1275 - 1285
• Main Authors: Valentinitsch, A., Trebeschi, S., Kaesmacher, J., Lorenz, C., Löffler, M. T., Zimmer, C., Baum, T., Kirschke, J. S.
• Format: Journal Article
• Language: English
• Published: London Springer London 01-06-2019; Springer Nature B.V
• Subjects: Aged; Algorithms; Bone Density - physiology; Bone mineral density; Classification; Computed tomography; Endocrinology; Feasibility Studies; Female; Fractures; Humans; Imaging, Three-Dimensional - methods; Incidental Findings; Male; Mass Screening - methods; Medical screening; Medicine; Medicine & Public Health; Middle Aged; Original; Original Article; Orthopedics; Osteoporosis; Osteoporosis - diagnostic imaging; Osteoporosis - physiopathology; Osteoporotic Fractures - diagnostic imaging; Osteoporotic Fractures - physiopathology; Radiographic Image Interpretation, Computer-Assisted - methods; Retrospective Studies; Rheumatology; Spinal Fractures - diagnostic imaging; Spinal Fractures - physiopathology; Spine; Tomography, X-Ray Computed - methods; Vertebrae; Random forest model; Osteoporosis; Texture analysis; Vertebral fractures; Quantitative computed tomography; Machine learning; BMD; Opportunistic screening
• ISSN: 0937-941X; 1433-2965
• DOI: 10.1007/s00198-019-04910-1

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.