Tuberculous and Brucellar Spondylodiscitis: Comparative Analysis of Clinical, Laboratory, and Radiological Features

Differentiation of Native Vertebral Osteomyelitis: A Comprehensive Review of Imaging Techniques and Future Applications

Native vertebral osteomyelitis, also termed spondylodiscitis, is an antibiotic-resistant disease that requires long-term treatment. Without proper treatment, NVO can lead to severe nerve damage or even death. Therefore, it is important to accurately diagnose the cause of NVO, especially in spontaneous cases. Infectious NVO is characterized by the involvement of 2 adjacent vertebrae and intervertebral discs, and common infectious agents include Staphylococcus aureus, Mycobacterium tuberculosis, Brucella abortus, and fungi. Clinical symptoms are generally nonspecific, and early diagnosis and appropriate treatment can prevent irreversible sequelae. Advances in pathologic histologic imaging have led physicians to look more forward to being able to differentiate between tuberculous and septic spinal discitis. Therefore, research in identifying and differentiating the imaging features of these 4 common NVOs is essential. Due to the diagnostic difficulties, clinical and radiologic diagnosis is the mainstay of provisional diagnosis. With the advent of the big data era and the emergence of convolutional neural network algorithms for deep learning, the application of artificial intelligence (AI) technology in orthopedic imaging diagnosis has gradually increased. AI can assist physicians in imaging review, effectively reduce the workload of physicians, and improve diagnostic accuracy. Therefore, it is necessary to present the latest clinical research on NVO and the outlook for future AI applications.

MRI-based interpretable radiomics nomogram for discrimination between Brucella spondylitis and Pyogenic spondylitis

Background

Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are commonly seen spinal infectious diseases. Both types can lead to vertebral destruction, kyphosis, and long-term neurological deficits if not promptly diagnosed and treated. Therefore, accurately diagnosis is crucial for personalized therapy. Distinguishing between PS and BS in everyday clinical settings is challenging due to the similarity of their clinical symptoms and imaging features. Hence, this study aims to evaluate the effectiveness of a radiomics nomogram using magnetic resonance imaging (MRI) to accurately differentiate between the two types of spondylitis.

Methods

Clinical and MRI data from 133 patients (2017-2022) with pathologically confirmed PS and BS (68 and 65 patients, respectively) were collected. We have divided patients into training and testing cohorts. In order to develop a clinical diagnostic model, logistic regression was utilized to fit a conventional clinical model (M1). Radiomics features were extracted from sagittal fat-suppressed T2-weighted imaging (FS-T2WI) sequence. The radiomics features were preprocessed, including scaling using Z-score and undergoing univariate analysis to eliminate redundant features. Furthermore, the Least Absolute Shrinkage and Selection Operator (LASSO) was employed to develop a radiomics score (M2). A composite model (M3) was created by combining M1 and M2. Subsequently, calibration and decision curves were generated to evaluate the nomogram's performance in both training and testing groups. The diagnostic performance of each model and the indication was assessed using the receiver operating curve (ROC) with its area under the curve (AUC). Finally, we used the SHapley Additive exPlanations (SHAP) model explanations technique to interpret the model result.

Results

We have finally selected 9 significant features from sagittal FS-T2WI sequences. In the differential diagnosis of PS and BS, the AUC values of M1, M2, and M3 in the testing set were 0.795, 0.859, and 0.868. The composite model exhibited a high degree of concurrence with the ideal outcomes, as evidenced by the calibration curves. The nomogram's possible clinical application values were indicated by the decision curve analysis. By using SHAP values to represent prediction outcomes, our model's prediction results are more understandable.

Conclusions

The implementation of a nomogram that integrates MRI and clinical data has the potential to significantly enhance the accuracy of discriminating between PS and BS within clinical settings.

Development and validation of a diagnostic model for differentiating tuberculous spondylitis from brucellar spondylitis using machine learning: A retrospective cohort study

Background

Tuberculous spondylitis (TS) and brucellar spondylitis (BS) are commonly observed in spinal infectious diseases, which are initially caused by bacteremia. BS is easily misdiagnosed as TS, especially in underdeveloped regions of northwestern China with less sensitive medical equipment. Nevertheless, a rapid and reliable diagnostic tool remains to be developed and a clinical diagnostic model to differentiate TS and BS using machine learning algorithms is of great significance.

Methods

A total of 410 patients were included in this study. Independent factors to predict TS were selected by using the least absolute shrinkage and selection operator (LASSO) regression model, permutation feature importance, and multivariate logistic regression analysis. A TS risk prediction model was developed with six different machine learning algorithms. We used several metrics to evaluate the accuracy, calibration capability, and predictability of these models. The performance of the model with the best predictability was further verified with the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the calibration curve. The clinical performance of the final model was evaluated by decision curve analysis.

Results

Six variables were incorporated in the final model, namely, pain severity, CRP, x-ray intervertebral disc height loss, x-ray endplate sclerosis, CT vertebral destruction, and MRI paravertebral abscess. The analysis of appraising six models revealed that the logistic regression model developed in the current study outperformed other methods in terms of sensitivity (0.88 ± 0.07) and accuracy (0.79 ± 0.07). The AUC of the logistic regression model predicting TS was 0.86 (95% CI, 0.81-0.90) in the training set and 0.86 (95% CI, 0.78-0.92) in the validation set. The decision curve analysis indicated that the logistic regression model displayed a higher clinical efficiency in the differential diagnosis.

Conclusions

The logistic regression model developed in this study outperformed other methods. The logistic regression model demonstrated by a calculator exerts good discrimination and calibration capability and could be applicable in differentiating TS from BS in primary health care diagnosis.

Chronological Analysis of Primary Cervical Spine Infection: A Single-Center Analysis of 59 Patients over Three Decades (1992–2018)

Primary cervical spine infections progress quickly and cause neurological impairment at an early stage. Despite their clinical significance, few studies have investigated primary cervical spine infections, owing to the rarity of the condition. This study analyzed the characteristics of 59 patients treated for primary cervical spine infections between 1992 and 2018 at our hospital. Clinical and radiological analyses were conducted. Moreover, a comparative analysis was performed, incorporating each patient’s underlying disease, mortality and complications, and treatment results. Comparison between groups based on the chronological period (1992–2000, 2001–2009, and 2010–2018) revealed that the mean age of onset has increased significantly in recent years. The rate of neurological impairment, duration of antibiotic use, and frequency of underlying disease increased significantly with time. No significant differences among groups were observed in the hematological and microbiological analyses. The incidence rate of epidural abscess and multisegmental infection increased significantly in recent years. There was no statistically significant difference in the complication and mortality rates, according to the time period. We think that prompt diagnosis and appropriate treatment are necessary, considering the current trends in primary cervical spine infection.

The Angiopoietin-like protein 4: a promising biomarker to distinguish brucella spondylitis from tuberculous spondylitis

Objective

The Angiopoietin-like protein 4 (ANGPTL-4) has been proved to be a protein associated with multiple inflammatory responses. Nevertheless, whether it contributes to distinguishing brucella spondylitis (BS) from tuberculous spondylitis (TS) remains an open question. Our study aim is to explore the capability of the ANGPTL-4 to differentiating BS from TS.

Materials and method

In our study, 53 patients were screened out according to the criteria precisely in Xinjiang Medical University Affiliated of the First Hospital from 1 January, 2016, to 31 December, 2018. Their clinical data were retrospectively reviewed. All of them underwent pathological biopsy and magnetic resonance imaging examination. All the frozen tissue sections were stained for testing ANGPTL-4.

Result

Among the 53 patients, BS had 26 patients, and TS had 27 patients. There was no significant difference between the baseline (P = 0.682) between the two groups. The positive rate of ANGPTL-4 in TS patients (24/27, 88.89%) was higher than that in BS patients (17/26, 65.83%) (P < 0.05). The incidence of microangiopathy and fibrous connective tissue hyperplasia in patients with BS was distinctly higher than those in the TS (P = 0.001, P = 0.008, respectively). Patients of TS frequently presented more granuloma, caseous necrosis, epithelial-like reaction, interleukin 6 (IL-6), and C-reactive protein (CRP) than those of BS.

Conclusion

Our study provided novel insights into distinguishing BS from TS using the ANGPTL-4 combining with histopathology, which may become new supporting evidence.

Key Points • Brucella spondylitis and tuberculous spondylitis are a significant public health concern and even have prolonged damage, contributing to severe health and economic outcomes in Xinjiang of China. • The granuloma, caseous necrosis, epithelioid reaction, microangiosis, and fibrous connective tissue of pathological tissue might play a critical significance for distinguishing brucella spondylitis from tuberculous spondylitis patients. • ANGPLT-4 may become new supporting evidence identify brucella spondylitis and tuberculous spondylitis which is implicated in inflammation angiogenesis-related disorders.