ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2018
Feasibility of opportunistic osteoporosis screening in routine contrast-enhanced multi detector computed tomography (MDCT) using texture analysis.
Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Pooled it
- Vertebral-level discrimination of incidental vertebral fractures using volumetric BMD, texture features, and finite element-derived fracture load: an exploratory study.Skeletal radiology · 2026Article
- Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.BMC medical imaging · 2025Article
- Advanced radiomic prediction of osteoporosis in primary hyperparathyroidism: a machine learning-based analysis of CT images.La Radiologia medica · 2025Article
- Feasibility study of opportunistic osteoporosis screening on chest CT using a multi-feature fusion DCNN model.Archives of osteoporosis · 2024Article
- Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images.Journal of healthcare engineering · 2024Article
- Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2023Article
- Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography.Bioengineering (Basel, Switzerland) · 2023Review
- 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
- The Application of Advanced Bone Imaging Technologies in Sports Medicine.Radiology research and practice · 2023Review
- A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images.BMC bioinformatics · 2022Article
- MR-based proton density fat fraction (PDFF) of the vertebral bone marrow differentiates between patients with and without osteoporotic vertebral fractures.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2022Article
- Multi-detector computed tomography (MDCT) imaging: association of bone texture parameters with finite element analysis (FEA)-based failure load of single vertebrae and functional spinal units.Quantitative imaging in medicine and surgery · 2021Article
- Association of Thigh Muscle Strength with Texture Features Based on Proton Density Fat Fraction Maps Derived from Chemical Shift Encoding-Based Water-Fat MRI.Diagnostics (Basel, Switzerland) · 2021Article
- Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength.Diagnostics (Basel, Switzerland) · 2021Article
- Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT.Frontiers in endocrinology · 2021Article
- Texture Analysis Using CT and Chemical Shift Encoding-Based Water-Fat MRI Can Improve Differentiation Between Patients With and Without Osteoporotic Vertebral Fractures.Frontiers in endocrinology · 2021Article
- Evaluation of Bone Marrow Texture and Trabecular Changes With Quantitative DCE-MRI and QCT in Alloxan-Induced Diabetic Rabbit Models.Frontiers in endocrinology · 2021Article
- Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT.Radiology. Cardiothoracic imaging · 2020Review
- X-ray-based quantitative osteoporosis imaging at the spine.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2020Review
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9 authors.
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Abstract
This study investigated the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. The results showed an acceptable reproducibility of texture features, and these features could discriminate healthy/osteoporotic fracture cohort with an accuracy of 83%.
introductionThis aim of this study is to investigate the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis.
methodsWe performed texture analysis at the spine in routine MDCT exams and investigated the effect of intravenous contrast medium (IVCM) (n = 7), slice thickness (n = 7), the long-term reproducibility (n = 9), and the ability to differentiate healthy/osteoporotic fracture cohort (n = 9 age and gender matched pairs). Eight texture features were extracted using gray level co-occurrence matrix (GLCM). The independent sample t test was used to rank the features of healthy/fracture cohort and classification was performed using support vector machine (SVM).
resultsThe results revealed significant correlations between texture parameters derived from MDCT scans with and without IVCM (r up to 0.91) slice thickness of 1 mm versus 2 and 3 mm (r up to 0.96) and scan-rescan (r up to 0.59). The performance of the SVM classifier was evaluated using 10-fold cross-validation and revealed an average classification accuracy of 83%.
conclusionsOpportunistic osteoporosis screening at the spine using specific texture parameters (energy, entropy, and homogeneity) and SVM can be performed in routine contrast-enhanced MDCT exams.
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