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Mertiri L, Freiling JT, Desai NK, Kralik SF, Huisman TAGM. Pediatric and adult meningeal, parenchymal, and spinal tuberculosis: A neuroimaging review. J Neuroimaging 2024; 34:179-194. [PMID: 38073450 DOI: 10.1111/jon.13177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/24/2023] [Accepted: 11/28/2023] [Indexed: 03/12/2024] Open
Abstract
Neurotuberculosis is defined as a tuberculous infection of the meninges, brain parenchyma, vessels, cranial and spinal nerves, spinal cord, skull, and spine that can occur either in a localized or in a diffuse form. It is a heterogeneous disease characterized by many imaging appearances and it has been defined as "the great mimicker" due to similarities with many other conditions. The diagnosis of central nervous system (CNS) tuberculosis (TB) is based on clinical presentation, neuroimaging findings, laboratory and microbiological findings, and comprehensive evaluation of the response to anti-TB drug treatment. However, the absence of specific symptoms, the wide spectrum of neurological manifestations, the myriad of imaging findings, possible inconclusive laboratory results, and the paradoxical reaction to treatment make the diagnosis often challenging and difficult, potentially delaying adequate treatment with possible devastating short-term and long-term neurologic sequelae. Familiarity with the imaging characteristics helps in accurate diagnosis and may prevent or limit significantly morbidity and mortality. The goal of this review is to provide a comprehensive up-to-date overview of the conventional and advanced imaging features of CNS TB for radiologists, neuroradiologists, and pediatric radiologists. We discuss the most typical neurotuberculosis imaging findings and their differential diagnosis in children and adults with the goal to provide a global overview of this entity.
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Affiliation(s)
- Livja Mertiri
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - John T Freiling
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Nilesh K Desai
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Stephen F Kralik
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Thierry A G M Huisman
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
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Wu S, Wei Y, Li H, Zhou C, Chen T, Zhu J, Liu L, Wu S, Ma F, Ye Z, Deng G, Yao Y, Fan B, Liao S, Huang S, Sun X, Chen L, Guo H, Chen W, Zhan X, Liu C. A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors. Infect Drug Resist 2022; 15:7327-7338. [PMID: 36536861 PMCID: PMC9758984 DOI: 10.2147/idr.s388868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/02/2022] [Indexed: 10/30/2023] Open
Abstract
OBJECTIVE The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS). METHODS A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort. t-test analysis, de-redundancy analysis, and minimum absolute shrinkage and selection operator (lasso) algorithm were utilized on the training set to obtain the optimal radiomics features from computed tomography (CT) for constructing the radiomics model and determine the radiomics score (Rad-score). Eight clinical risk predictors were identified to develop the clinical model. Combined with clinical risk predictors and Rad-scores, a nomogram model was constructed using multivariate logistic regression analysis. RESULTS A total of 1781 features were extracted, and 12 optimal radiomic features were utilized to construct the radiomic model and determine the Rad-score. The combined clinical radiomics model revealed good discrimination performance in both the training cohort and the validation cohort (AUC = 0.891 and 0.830) and was superior to the clinical (AUC = 0.807 and 0.785) and radiomics (AUC = 0.796 and 0.811) models. The calibration curve and DCA also depicted that the nomogram had better clinical efficacy. The discriminative performance of the model is well validated in the test cohort (AUC=0.877). CONCLUSION The clinical radiomic nomogram could serve as a promising predictive tool for differentiating TS from PS, which could be helpful for clinical decision-making.
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Affiliation(s)
- Shaofeng Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Yating Wei
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hao Li
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chenxing Zhou
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Tianyou Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Jichong Zhu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Lu Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Siling Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Fengzhi Ma
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Zhen Ye
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Guobing Deng
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Yuanlin Yao
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Binguang Fan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shian Liao
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shengsheng Huang
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xuhua Sun
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Liyi Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hao Guo
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Wuhua Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xinli Zhan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chong Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
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Liu X, Zheng M, Sun J, Cui X. A diagnostic model for differentiating tuberculous spondylitis from pyogenic spondylitis on computed tomography images. Eur Radiol 2021; 31:7626-7636. [PMID: 33768287 DOI: 10.1007/s00330-021-07812-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 01/01/2021] [Accepted: 02/18/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To develop and evaluate a logistics regression diagnostic model based on computer tomography (CT) features to differentiate tuberculous spondylitis (TS) from pyogenic spondylitis (PS). METHODS Demographic and clinical features were collected from the Electronic Medical Record System. Data of bony changes seen on CT images were compared between the PS (n = 61) and TS (n = 51) groups using the chi-squared test or t test. Based on features that were identified to be significant, a diagnostic model was developed from a derivation set (two thirds) and evaluated in a validation set (one third). The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS The width of bone formation around the vertebra and sequestrum was greater in the TS group. There were significant differences between the two groups in the horizontal and longitudinal location of erosion and the morphology of axial bone destruction and sagittal residual vertebra. Kyphotic deformity and overlapping vertebrae were more common in the TS group. A diagnostic model that included eight predictors was developed and simplified to include the following six predictors: width of the bone formation surrounding the vertebra, longitudinal location, axial-specific erosive morphology, specific morphology of the residual vertebra, kyphotic deformity, and overlapping vertebrae. The simplified model showed good sensitivity, specificity, and total accuracy (85.59%, 87.80%, and 86.50%, respectively); the AUC was 0.95, indicating good clinical predictive ability. CONCLUSIONS A diagnostic model based on bone destruction and formation seen on CT images can facilitate clinical differentiation of TS from PS. KEY POINTS • We have developed and validated a simple diagnostic model based on bone destruction and formation observed on CT images that can differentiate tuberculous spondylitis from pyogenic spondylitis. • The model includes six predictors: width of the bone formation surrounding the vertebra, longitudinal location, axial-specific erosive morphology, specific morphology of the residual vertebra, kyphotic deformity, and overlapping vertebrae. • The simplified model has good sensitivity, specificity, and total accuracy with a high AUC, indicating excellent predictive ability.
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Affiliation(s)
- Xiaoyang Liu
- Department of Spine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 9677 in Jingshi Road, Jinan City, China
| | - Meimei Zheng
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Jianmin Sun
- Department of Spine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 9677 in Jingshi Road, Jinan City, China
| | - Xingang Cui
- Department of Spine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 9677 in Jingshi Road, Jinan City, China.
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Salvador GLO, Basso ACN, Barbieri PP, Leitao CA, Teixeira BCA, Neto AC. Central nervous system and spinal cord tuberculosis: Revisiting an important disease. Clin Imaging 2020; 69:158-168. [PMID: 32853843 DOI: 10.1016/j.clinimag.2020.07.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 07/16/2020] [Accepted: 07/27/2020] [Indexed: 12/13/2022]
Abstract
Tuberculosis is a worldwide pandemic. Estimated that about 25% humans are colonized by Mycobacterium tuberculosis and about 1% are believed to develop the infection in the central nervous system (CNS-TB). Given the importance of this disease and its high levels of morbidity and mortality, it is imperative that every radiologist must be reminded of the most common findings of CNS-TB as there are several related differential diagnoses for this disease. The most common form CNS-TB is tuberculous meningitis (TBM), characterized mostly by basal meningitis, but infarction, hydrocephalus and tuberculomas could be present. Intracerebral tuberculosis is characterized by tuberculomas that can have different imaging features according to their stage. Vascular and spinal complications of tuberculosis are also reported. This review compiles the classic and unusual findings regarding CNS-TB together with new diagnostic scores in which neuroimaging have an important role.
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Affiliation(s)
- Gabriel L O Salvador
- Department of Diagnostic Radiology, Hospital de Clinicas, Federal University of Parana, Curitiba, Parana, Brazil.
| | - Ana C N Basso
- Department of Diagnostic Radiology, Hospital de Clinicas, Federal University of Parana, Curitiba, Parana, Brazil
| | - Poliana P Barbieri
- Department of Diagnostic Radiology, Hospital de Clinicas, Federal University of Parana, Curitiba, Parana, Brazil
| | - Cleverson A Leitao
- Department of Diagnostic Radiology, Hospital de Clinicas, Federal University of Parana, Curitiba, Parana, Brazil
| | - Bernardo C A Teixeira
- Department of Diagnostic Radiology, Hospital de Clinicas, Federal University of Parana, Curitiba, Parana, Brazil
| | - Arnolfo C Neto
- Department of Diagnostic Radiology, Hospital de Clinicas, Federal University of Parana, Curitiba, Parana, Brazil
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