ArticleBMC infectious diseases2025
Multimodal imaging-based interpretable radiomics for differentiating brucella and tuberculosis spondylitis: a two-center study.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Computer-aided detection of equivocal spinal tuberculosis on X-ray using a YOLOv11-based deep learning model.Frontiers in public health · 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
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10 authors.
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Abstract
backgroundSpondylitis, particularly infectious forms caused by Mycobacterium tuberculosis and Brucella species, presents significant clinical challenges due to overlapping symptoms and diagnostic difficulties. Accurate differentiation is crucial for effective treatment, necessitating advanced imaging techniques and radiomics to enhance diagnostic precision and improve patient outcomes in cases of tuberculosis spondylitis (TS) and brucella spondylitis (BS).
methodsThis retrospective cohort study included 195 patients diagnosed with TS or BS from January 2020 to December 2024 in center1, with an external validation cohort of 57 patients in center 2. Inclusion criteria consisted of relevant clinical symptoms for at least six months, positive serological tests, Magnetic Resonance Imaging (MRI) abnormalities, and complete medical records. Imaging was performed at two centers, employing standardized protocols for T1-weighted(T1WI) and T2-weighted(T2WI) and fat-suppression T2WI (FS T2WI) MRI. Region of Interest (ROI) segmentation and radiomics feature extraction were conducted using the Deepwise platform, yielding a total of 1049 Computed Tomography (CT) and 5829 MRI features. Nine predictive models were developed and validated through nested five-fold cross-validation, assessing performance metrics such as Area Under the Curve (AUC), sensitivity, and specificity. Statistical significance was set at p < 0.05.
resultsA total of 195 patients with 207 lesions were analyzed, comprising 116 patients of TS and 79 patients of BS. An external validation cohort included 57 patients with 60 lesions. Nine predictive models were developed using selected features: the CT model utilized 80 features, while T1WI, T2WI, and FS T2WI models employed 14, 33, and 32 features, respectively. Multi-modality models (CT, T1WI, T2WI and FS T2WI model) combined features from various sequences, achieving optimal performance with an AUC of 0.8136 in validation dataset. Model efficacy was validated through receiver operating characteristic (ROC) curve analysis, decision curve analyses, and calibration plots. Additionally, SHapley Additive exPlanations(SHAP) analysis was used to interpret model predictions, identifying key influential features, which are detailed in the supplementary materials.
conclusionOur research indicates that multimodal imaging-based radiomics hold significant potential for the differential diagnosis of BS and TS.
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