Benign and malignant VFs are characterized by microarchitectural deterioration of osseous tissue and decreased bone mineral density (BMD) with associated fracture risk, since the three-dimensional (3D) trabecular bone architecture is impaired in both entities. Up to 10% of all cancer patients develop symptomatic bone metastases during the course of the illness, and vertebral metastases account for up to 39% of all bone metastases.

The differentiation between benign (osteoporotic) vertebral fractures (VFs) on the one hand, and malignant VFs due to underlying metastases on the other hand, based on CT imaging only is a frequent challenge in clinical practice and of emerging importance within the aging population. Recently, TA methods have been used in different radiological subfields, such as neuroimaging or musculoskeletal imaging providing additional information regarding diagnosis or patients’ outcomes in various diseases. Thus, TA enables the extraction of additional diagnostic, predictive, and prognostic information beyond what is visually perceptive. Texture features (TFs) allow for quantitative characterization of image properties, such as uniformity, heterogeneity, and randomness, as well as repetitive image patterns. Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.ĬT-based texture analysis (TA) is a growing and representative subfield of radiomics and represents both a non-invasive and quantitative method for the assessment of medical images.

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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