ArticleThe Journal of international medical research2023
MRI radiomics-based evaluation of tuberculous and brucella spondylitis.
Article in The Journal of international medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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, 19 citations in OpenAlex.
- Quantifying research hotspots and trends in brucella spondylitis: a bibliometric analysis.Frontiers in surgery · 2024Pooled it
- Article
- Infectious Spondylodiscitis of Bacterial Causes in Adults: Epidemiology, Pathophysiology, Diagnostic and Treatment Challenges.Microorganisms · 2026Review
- Four MRI-Based Radiomics Models for Diagnosis of Lumbar Intervertebral Disc Degeneration.Journal of imaging informatics in medicine · 2026Article
- Integration of single-cell and bulk RNA sequencing reveals divergent T-cell immune circuits and a shared osteo-immune pathway in spinal tuberculosis and brucellar spondylitis.Frontiers in cellular and infection microbiology · 2026Article
- Preoperative differentiation of spinal tuberculosis, pyogenic, and brucellar spondylitis: a multimodal machine learning study across five centers.Frontiers in cellular and infection microbiology · 2026Article
- Comprehensive comparative analysis of explainable deep learning model for differentiation of brucellar spondylitis and tuberculous spondylitis through MRI sequences.European journal of medical research · 2025Article
- Multimodal-Imaging-Based Interpretable Deep Learning Framework for Distinguishing Brucella from Tuberculosis Spondylitis: A Dual-Center Study.Diagnostics (Basel, Switzerland) · 2025Article
- Multimodal imaging-based interpretable radiomics for differentiating brucella and tuberculosis spondylitis: a two-center study.BMC infectious diseases · 2025Article
- Precision Through Detail: Radiomics and Windowing Techniques as Key for Detecting Dens Axis Fractures in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Treatment of lumbar tuberculosis with minimally invasive anterior lesion clearance combined with posterior fixation.World journal of orthopedics · 2025Article
- Senility, defecation disorders, sleep disorders, and non-operative spinal infections: A single-center retrospective analysis.World journal of orthopedics · 2025Article
- CT-based 3D Super-resolution Radiomics for the Differential Diagnosis of BrucellaCurrent medical imaging · 2025Article
- Imaging of spinal and central nervous system brucellosis: a review.Polish journal of radiology · 2025Review
- Applications of Artificial Intelligence and Machine Learning in Spine MRI.Bioengineering (Basel, Switzerland) · 2024Review
- Optimizing diagnosis and surgical decisions for chronic osteomyelitis through radiomics in the precision medicine era.Frontiers in bioengineering and biotechnology · 2024Article
- A Novel Differentiation Nomogram Model for Brucellar Spondylitis and Tuberculous Spondylitis.Infection and drug resistance · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe analyzed magnetic resonance imaging (MRI) and radiomics labels from tuberculous spondylitis (TBS) and brucella spondylitis (BS) to build machine learning models that differentiate TBS from BS and culture-positive TBS (TBS(+)) from culture-negative TBS (TBS(-).
methodsThis retrospective study included 56 patients with BS, 63 patients with TBS(+) and 71 patients with TBS(-). Radiomics labels were extracted from T2-weighted fat-suppression images. MRI labels were analyzed via logistic regression (LR); radiomics labels were analyzed by t-tests, SelectKBest, and least absolute shrinkage and selection operator (LASSO). Random forest (RF) and support vector machine (SVM) models were established using radiomics or joint (radiomics+MRI) labels. Models were evaluated by receiver operating characteristic curves, areas under the curve (AUCs), decision curve analysis (DCA), and Hosmer-Lemeshow tests.
resultsWhen joint-label models were used to compare BS vs TBS(+) and BS vs TBS(-) groups, SVM AUCs were 0.904 and 0.944, respectively, whereas RF AUCs were 0.950 and 0.947, respectively; these were higher than the AUCs of the MRI label-based LR model. DCA showed that radiomics-based machine learning models had a greater net benefit; Hosmer-Lemeshow tests demonstrated good prediction consistency for all models.
conclusionsRadiomics can help distinguish TBS from BS and TBS(+) from TBS(-).
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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.