Skewness global showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 malignant fracture group: 0.59 and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs. Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs. Eight TFs were extracted: Variance global, Skewness global, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( ). VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. MethodsĪ total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs).
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