ArticleEuropean radiology2023
Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist's assessment.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Artificial intelligence in risk prediction and diagnosis of vertebral fractures.Scientific reports · 2024Pooled it
- Performance Comparison of Machine Learning Using Radiomic Features and CNN-Based Deep Learning in Benign and Malignant Classification of Vertebral Compression Fractures Using CT Scans.Journal of imaging informatics in medicine · 2026Article
- Diagnostic value of computed tomography plus magnetic resonance imaging in assessing the benign and malignant nature of vertebral compression fractures.Frontiers in medicine · 2026Article
- MRI-based habitat analysis for differentiating benign and malignant vertebral compression fractures.BMC medical imaging · 2025Article
- Current applications of deep learning in vertebral fracture diagnosis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Review
- Deep Learning-Based Differentiation of Vertebral Body Lesions on Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- MRI-based 2.5D deep learning radiomics nomogram for the differentiation of benign versus malignant vertebral compression fractures.Frontiers in oncology · 2025Article
- Applications of Artificial Intelligence and Machine Learning in Spine MRI.Bioengineering (Basel, Switzerland) · 2024Review
- Leveraging 3D Convolutional Neural Networks for Accurate Recognition and Localization of Ankle Fractures.Therapeutics and clinical risk management · 2024Article
- Value ofEJNMMI research · 2023Article
Corrections and comments
- Commented on by
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo test the diagnostic performance of a deep-learning Two-Stream Compare and Contrast Network (TSCCN) model for differentiating benign and malignant vertebral compression fractures (VCFs) based on MRI.
methodsWe tested a deep-learning system in 123 benign and 86 malignant VCFs. The median sagittal T1-weighted images (T1WI), T2-weighted images with fat suppression (T2WI-FS), and a combination of both (thereafter, T1WI/T2WI-FS) were used to validate TSCCN. The receiver operator characteristic (ROC) curve was analyzed to evaluate the performance of TSCCN. The accuracy, sensitivity, and specificity of TSCCN in differentiating benign and malignant VCFs were calculated and compared with radiologists' assessments. Intraclass correlation coefficients (ICCs) were tested to find intra- and inter-observer agreement of radiologists in differentiating malignant from benign VCFs.
resultsThe AUC of the ROC plots of TSCCN according to T1WI, T2WI-FS, and T1WI/T2WI-FS images were 99.2%, 91.7%, and 98.2%, respectively. The accuracy of T1W, T2WI-FS, and T1W/T2WI-FS based on TSCCN was 95.2%, 90.4%, and 96.2%, respectively, greater than that achieved by radiologists. Further, the specificity of T1W, T2WI-FS, and T1W/T2WI-FS based on TSCCN was higher at 98.4%, 94.3%, and 99.2% than that achieved by radiologists. The intra- and inter-observer agreements of radiologists were 0.79-0.85 and 0.79-0.80 for T1WI, 0.65-0.72 and 0.70-0.74 for T2WI-FS, and 0.83-0.88 and 0.83-0.84 for T1WI/T2WI-FS.
conclusionThe TSCCN model showed better diagnostic performance than radiologists for automatically identifying benign or malignant VCFs, and is a potentially helpful tool for future clinical application. CLINICAL RELEVANCE STATEMENT: TSCCN-assisted MRI has shown superior performance in distinguishing benign and malignant vertebral compression fractures compared to radiologists. This technology has the value to enhance diagnostic accuracy, sensitivity, and specificity. Further integration into clinical practice is required to optimize patient management. KEY POINTS: • The Two-Stream Compare and Contrast Network (TSCCN) model showed better diagnostic performance than radiologists for identifying benign vs malignant vertebral compression fractures. • The processing of TSCCN is fast and stable, better than the subjective evaluation by radiologists in diagnosing vertebral compression fractures. • The TSCCN model provides options for developing a fully automated, streamlined artificial intelligence diagnostic tool.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.