ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
Multimodal imaging fusion and machine learning model development: differential diagnosis of spinal inflammatory lesions using combined CT hounsfield units and MRI features.
Article in 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, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Etiologic spectrum and diagnostic gaps of spinal infections: a cross-sectional study in Qingdao, China.Preventive medicine reports · 2026Article
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14 authors.
Funding
Abstract
objectiveThe objective is to develop a differential diagnosis model for tuberculous spondylitis (TS) and pyogenic spondylitis (PS) by integrating MRI morphological features and computed tomography (CT) density parameters (Hounsfield Units, HU). This study aims to leverage multimodal data complementarity to achieve fusion of qualitative and quantitative information, thereby providing clinicians with a rapid and objective decision support tool for spinal inflammatory lesion characterization.
methodsImaging data were extracted from MRI and CT scans of patients with TS and PS, then compared and summarized. Receiver operating characteristic (ROC) curves were used to determine optimal HU value thresholds. The least absolute shrinkage and selection operator (Lasso) regression was applied to identify the most predictive features for model construction. A logistic regression-based predictive model was developed and visualized as a nomogram. Model validation was performed using bootstrap resampling, ROC analysis, and decision curve analysis (DCA).
resultsA total of 171 patients with TS (n = 91) or PS (n = 80) were included. Statistically significant differences in MRI features were observed between the two groups (P < 0.05). Additionally, significant HU value differences were found in diseased vertebral endplates, small cavitary abscesses, large cavitary abscesses, and intravertebral abscesses between TS and PS patients (P < 0.05). The predictive model incorporated seven independent predictors. Calibration curves, ROC analysis, and DCA all demonstrated excellent model performance.
conclusionCombined MRI and CT HU value analysis effectively differentiates TS from PS. The predictive model integrating imaging features and quantitative parameters demonstrates high accuracy and clinical utility, offering a novel approach to optimize diagnostic and treatment strategies for spinal infectious diseases.
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