ArticleFrontiers in endocrinology2021
Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT.
Article in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Two-decade dialogue between artificial intelligence and osteoporosis: research trajectories and frontier projections under bibliometric and visual analysis.Frontiers in medicine · 2025Pooled it
- Texture analysis on muscle T2 maps can reveal changes in upper trapezius muscles related to primary headache disorders.Scientific reports · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Machine learning integration of multi-modal radiomics and clinical factors predicts refracture risk after percutaneous kyphoplasty in postmenopausal women.Scientific reports · 2025Article
- A Conditional Generative Diffusion Model of Trabecular Bone with Tunable Microstructure.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Time-efficient combined morphologic and quantitative joint MRI: an in situ study of standardized knee cartilage defects in human cadaveric specimens.European radiology experimental · 2024Article
- Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography.Diagnostics (Basel, Switzerland) · 2023Article
- Assessing bone mineral density in non-weight bearing regions of the body: a texture analysis approach using abdomen and pelvis computed tomography Hounsfield units-a cross-sectional study.Quantitative imaging in medicine and surgery · 2023Article
- Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework.Quantitative imaging in medicine and surgery · 2023Article
- Addressing Challenges of Opportunistic Computed Tomography Bone Mineral Density Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT.Diagnostics (Basel, Switzerland) · 2023Article
- Application of preoperative CT texture analysis in papillary gastric adenocarcinoma.BMC cancer · 2022Article
- Development and validation of multivariate models integrating preoperative clinicopathological and radiographic findings to predict HER2 status in gastric cancer.Scientific reports · 2022Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To identify long-term reproducible texture features (TFs) of spinal computed tomography (CT), and characterize variations with regard to gender, age and vertebral level using our automated quantification framework. Methods: We performed texture analysis (TA) on baseline and follow-up CT (follow-up duration: 30-90 days) of 21 subjects (8 females, 13 males, age at baseline 61.2 ± 9.2 years) to determine long-term reproducibility. TFs with a long-term reproducibility error Δ Results: Six of the 24 extracted TFs had Δ Conclusion: Long-term reproducible CT-based TFs of the thoracolumbar spine were established and characterized in a predominantly older adult study population. The gender-, age- and vertebral-level-specific values may serve as foundation for osteoporosis diagnostics and facilitate future studies investigating vertebral microstructure.
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