Goller, Sophia S; Foreman, Sarah C; Rischewski, Jon F; Weißinger, Jürgen; Dietrich, Anna-Sophia; Schinz, David; Stahl, Robert; Luitjens, Johanna; Siller, Sebastian; Schmidt, Vanessa F; Erber, Bernd; Ricke, Jens; Liebig, Thomas; Kirschke, Jan S; Dieckmeyer, Michael; Gersing, Alexandra S (2023). Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features. European spine journal, 32(12), pp. 4314-4320. Springer 10.1007/s00586-023-07838-7
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PURPOSE
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).
METHODS
A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs.
RESULTS
Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs.
CONCLUSION
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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology |
UniBE Contributor: |
Dieckmeyer, Michael |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1432-0932 |
Publisher: |
Springer |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
05 Jul 2023 08:52 |
Last Modified: |
22 Nov 2023 00:12 |
Publisher DOI: |
10.1007/s00586-023-07838-7 |
PubMed ID: |
37401945 |
Uncontrolled Keywords: |
Automated segmentation Bone microstructure Computed tomography Convolutional neural network Texture analysis |
BORIS DOI: |
10.48350/184460 |
URI: |
https://boris.unibe.ch/id/eprint/184460 |