Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features.

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)

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

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