ArticleScientific reports2022
Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in risk prediction and diagnosis of vertebral fractures.Scientific reports · 2024Pooled it
- Automated vs. manual segmentation for small renal mass imaging.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Article
- Development and Validation of an AI-Integrated System for Automated Fracture Detection and Pedicle Puncture Planning in Lumbar Osteoporotic Vertebral Compression Fractures Based on the Nine-Grid Area Division Method.Orthopaedic surgery · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Using artificial intelligence for distinguishing benign and malignant vertebral compression fractures by computed tomography: a scoping review.International journal of burns and trauma · 2026Review
- High-Fidelity Finite Element Modeling Technique to Improve Sensitivity to Bone Tissue Changes of Older Adults with Obesity undergoing Intensive Lifestyle Intervention.Annals of biomedical engineering · 2025Article
- Deep learning model for automated detection of fresh and old vertebral fractures on thoracolumbar CT.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 · 2025Article
- MRI-based 2.5D deep learning radiomics nomogram for the differentiation of benign versus malignant vertebral compression fractures.Frontiers in oncology · 2025Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images.Journal of imaging informatics in medicine · 2024Article
- Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.European radiology · 2024Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Autofluorescence-based tissue characterization enhances clinical prospects of light-sheet-microscopy.Scientific reports · 2024Article
- Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients.Scientific reports · 2024Article
- Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis.Journal of clinical medicine · 2024Review
- 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
- Accurate Differentiation of Spinal Tuberculosis and Spinal Metastases Using MR-Based Deep Learning Algorithms.Infection and drug resistance · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although CT radiomics has shown promising results in the evaluation of vertebral fractures, the need for manual segmentation of fractured vertebrae limited the routine clinical implementation of radiomics. Therefore, automated segmentation of fractured vertebrae is needed for successful clinical use of radiomics. In this study, we aimed to develop and validate an automated algorithm for segmentation of fractured vertebral bodies on CT, and to evaluate the applicability of the algorithm in a radiomics prediction model to differentiate benign and malignant fractures. A convolutional neural network was trained to perform automated segmentation of fractured vertebral bodies using 341 vertebrae with benign or malignant fractures from 158 patients, and was validated on independent test sets (internal test, 86 vertebrae [59 patients]; external test, 102 vertebrae [59 patients]). Then, a radiomics model predicting fracture malignancy on CT was constructed, and the prediction performance was compared between automated and human expert segmentations. The algorithm achieved good agreement with human expert segmentation at testing (Dice similarity coefficient, 0.93-0.94; cross-sectional area error, 2.66-2.97%; average surface distance, 0.40-0.54 mm). The radiomics model demonstrated good performance in the training set (AUC, 0.93). In the test sets, automated and human expert segmentations showed comparable prediction performances (AUC, internal test, 0.80 vs 0.87, p = 0.044; external test, 0.83 vs 0.80, p = 0.37). In summary, we developed and validated an automated segmentation algorithm that showed comparable performance to human expert segmentation in a CT radiomics model to predict fracture malignancy, which may enable more practical clinical utilization of radiomics.
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