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Shi H, Prayer D, Kienast P, Khalaveh F, Tischer J, Binder J, Weber M, Stuempflen M, Kasprian G. Revisiting the Pathophysiology of Intracranial Hemorrhage in Fetuses with Chiari II Malformation: Novel Imaging Biomarkers of Disease Severity? AJNR Am J Neuroradiol 2024:ajnr.A8331. [PMID: 38719608 DOI: 10.3174/ajnr.a8331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 05/02/2024] [Indexed: 08/24/2024]
Abstract
BACKGROUND AND PURPOSE Intracranial hemorrhage (ICH) has emerged as a notable concern in Chiari II malformation (CM II), yet its origins and clinical implications remain elusive. This study aims to validate the in utero prevalence of ICH in CM II and investigate contributing factors, and visualize the findings in a network format. MATERIALS AND METHODS A single-center retrospective review of fetal MRI scans obtained in fetuses with CM II (presenting January 2007 to December 2022) was performed for ICH utilizing EPI-T2* blood-sensitive sequence. Fetuses with aqueduct stenosis (AS) were included as a control group. The incidence of ICH and corresponding gestational ages were compared between CM II and AS cases, and morphometric measurements (inner/outer CSF spaces, posterior fossa, venous structure) were compared among the 4 1:1 age-matched groups: CM II+ICH, CM II-ICH, AS+ICH, and AS-ICH. Additionally, a co-occurrence network was constructed to visualize associations between phenotypic features in ICH cases. RESULTS A total of 101 fetuses with CM II and 90 controls with AS at a median gestational age of 24.4 weeks and 22.8 weeks (P = .138) were included. Prevalence of ICH in fetuses with CM II was higher compared with the AS cases (28.7% versus 18.9%, P = .023), accompanied by congested veins (deep vein congestion mainly in young fetuses, and cortical veins may also be affected in older fetuses). ICH was notably correlated with specific anatomic features, essentially characterized by reduced outer CSF spaces and clivus-supraocciput angle. The co-occurrence network analysis reveals complex connections including bony defects, small posterior fossa dimensions, vermis ectopia, reduced CSF spaces, as well as venous congestion and venous sinus stenosis as pivotal components within the network. CONCLUSIONS The high prevalence of ICH-detected by fetal MRI-among fetuses with CM emphasizes the pathophysiologic importance of venous congestion, ICH, and vasogenic edema. As indicators of disease severity, these features may serve as helpful additional imaging biomarkers for the identification of potential candidates for fetal surgery.
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Affiliation(s)
- Hui Shi
- From the Department of Radiology (H.S.), Zhu Jiang Hospital, Southern Medical University, Guangzhou, China
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-guided Therapy (D.P., P.K., J.T., M.W., M.S., G.K.), Medical University of Vienna, Vienna, Austria
| | - Patric Kienast
- Department of Biomedical Imaging and Image-guided Therapy (D.P., P.K., J.T., M.W., M.S., G.K.), Medical University of Vienna, Vienna, Austria
| | - Farjad Khalaveh
- Department of Neurosurgery (F.K.), Medical University of Vienna, Vienna, Austria
| | - Johannes Tischer
- Department of Biomedical Imaging and Image-guided Therapy (D.P., P.K., J.T., M.W., M.S., G.K.), Medical University of Vienna, Vienna, Austria
| | - Julia Binder
- Department of Obstetrics and Feto-maternal Medicine (J.B.), Medical University of Vienna, Vienna, Austria
| | - Michael Weber
- Department of Biomedical Imaging and Image-guided Therapy (D.P., P.K., J.T., M.W., M.S., G.K.), Medical University of Vienna, Vienna, Austria
| | - Marlene Stuempflen
- Department of Biomedical Imaging and Image-guided Therapy (D.P., P.K., J.T., M.W., M.S., G.K.), Medical University of Vienna, Vienna, Austria
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-guided Therapy (D.P., P.K., J.T., M.W., M.S., G.K.), Medical University of Vienna, Vienna, Austria
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Shi H, Prayer F, Kienast P, Khalaveh F, Nasel C, Binder J, Watzenboeck ML, Weber M, Prayer D, Kasprian G. Multiparametric prenatal imaging characterization of fetal brain edema in Chiari II malformation might help to select candidates for fetal surgery. Eur Radiol 2024:10.1007/s00330-024-10729-0. [PMID: 38656710 DOI: 10.1007/s00330-024-10729-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVE To identify brain edema in fetuses with Chiari II malformation using a multiparametric approach including structural T2-weighted, diffusion tensor imaging (DTI) metrics, and MRI-based radiomics. METHODS A single-center retrospective review of MRI scans obtained in fetuses with Chiari II was performed. Brain edema cases were radiologically identified using the following MR criteria: brain parenchymal T2 prolongation, blurring of lamination, and effacement of external CSF spaces. Fractional anisotropy (FA) values were calculated from regions of interest (ROI), including hemispheric parenchyma, internal capsule, and corticospinal tract, and compared group-wise. After 1:1 age matching and manual single-slice 2D segmentation of the fetal brain parenchyma using ITK-Snap, radiomics features were extracted using pyradiomics. Areas under the curve (AUCs) of the features regarding discriminating subgroups were calculated. RESULTS Ninety-one fetuses with Chiari II underwent a total of 101 MRI scans at a median gestational age of 24.4 weeks and were included. Fifty scans were visually classified as Chiari II with brain edema group and showed significantly reduced external CSF spaces compared to the nonedema group (9.8 vs. 18.3 mm, p < 0.001). FA values of all used ROIs were elevated in the edema group (p < 0.001 for all ROIs). The 10 most important radiomics features showed an AUC of 0.81 (95%CI: 0.71, 0.91) for discriminating between Chiari II fetuses with and without edema. CONCLUSIONS Brain edema in fetuses with Chiari II is common and radiologically detectable on T2-weighted fetal MRI sequences, and DTI-based FA values and radiomics features provide further evidence of microstructure differences between subgroups with and without edema. CLINICAL RELEVANCE STATEMENT A more severe phenotype of fetuses with Chiari II malformation is characterized by prenatal brain edema and more postnatal clinical morbidity and disability. Fetal brain edema is a promising prenatal MR imaging biomarker candidate for optimizing the risk-benefit evaluation of selection for fetal surgery. KEY POINTS Brain edema of fetuses prenatally diagnosed with Chiari II malformation is a common, so far unknown, association. DTI metrics and radiomics confirm microstructural differences between the brains of Chiari II fetuses with and without edema. Fetal brain edema may explain worse motor outcomes in this Chiari II subgroup, who may substantially benefit from fetal surgery.
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Affiliation(s)
- Hui Shi
- Department of Radiology, Zhujiang Hospital, Southern Medical University, No. 253, Industrial Road, Guangzhou, China
| | - Florian Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Patric Kienast
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Farjad Khalaveh
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Christian Nasel
- Department of Radiology (Diagnostic and Interventional) (C.N.), University Hospital Tulln - Karl Landsteiner Private University of Health Sciences, Alter Ziegelweg 10, 3430, Tulln, Austria
| | - Julia Binder
- Department of Obstetrics and Feto-maternal Medicine, Medical University of Vienna, Vienna, Austria
| | - Martin L Watzenboeck
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Michael Weber
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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Payette K, Li HB, de Dumast P, Licandro R, Ji H, Siddiquee MMR, Xu D, Myronenko A, Liu H, Pei Y, Wang L, Peng Y, Xie J, Zhang H, Dong G, Fu H, Wang G, Rieu Z, Kim D, Kim HG, Karimi D, Gholipour A, Torres HR, Oliveira B, Vilaça JL, Lin Y, Avisdris N, Ben-Zvi O, Bashat DB, Fidon L, Aertsen M, Vercauteren T, Sobotka D, Langs G, Alenyà M, Villanueva MI, Camara O, Fadida BS, Joskowicz L, Weibin L, Yi L, Xuesong L, Mazher M, Qayyum A, Puig D, Kebiri H, Zhang Z, Xu X, Wu D, Liao K, Wu Y, Chen J, Xu Y, Zhao L, Vasung L, Menze B, Cuadra MB, Jakab A. Fetal brain tissue annotation and segmentation challenge results. Med Image Anal 2023; 88:102833. [PMID: 37267773 DOI: 10.1016/j.media.2023.102833] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 03/16/2023] [Accepted: 04/20/2023] [Indexed: 06/04/2023]
Abstract
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.
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Affiliation(s)
- Kelly Payette
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.
| | - Hongwei Bran Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Roxane Licandro
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, United States; Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab (CIR), Medical University of Vienna, Vienna, Austria
| | - Hui Ji
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | | | | | | | - Hao Liu
- Shanghai Jiaotong University, China
| | | | | | - Ying Peng
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Huiquan Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Guiming Dong
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Fu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - ZunHyan Rieu
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Hyun Gi Kim
- Department of Radiology, The Catholic University of Korea, Eunpyeong St. Mary's Hospital, Seoul 06247, South Korea
| | - Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Ali Gholipour
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Helena R Torres
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - Bruno Oliveira
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - João L Vilaça
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Yang Lin
- Department of Computer Science, Hong Kong University of Science and Technology, China
| | - Netanell Avisdris
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel; Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel
| | - Ori Ben-Zvi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel
| | - Dafna Ben Bashat
- Sagol School of Neuroscience, Tel Aviv University, Israel; Sackler Faculty of Medicine, Tel Aviv University, Israel
| | - Lucas Fidon
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Michael Aertsen
- Department of Radiology, University Hospitals Leuven, Leuven 3000, Belgium
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Mireia Alenyà
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Maria Inmaculada Villanueva
- Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Oscar Camara
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Bella Specktor Fadida
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Liao Weibin
- School of Computer Science, Beijing Institute of Technology, China
| | - Lv Yi
- School of Computer Science, Beijing Institute of Technology, China
| | - Li Xuesong
- School of Computer Science, Beijing Institute of Technology, China
| | - Moona Mazher
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | | | - Domenec Puig
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Zelin Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | | | - Yixuan Wu
- Zhejiang University, Hangzhou, China
| | | | - Yunzhi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Lana Vasung
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, United States; Department of Pediatrics, Harvard Medical School, United States
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland; University Research Priority Project Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
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Zhu H, Li Z, Zhou Y, Zheng R, Diao C, Li K, Feng Q, Wang D. Neutrophil-lymphocyte ratio as a risk factor for osteoporotic vertebrae fractures and femoral neck fractures. Medicine (Baltimore) 2022; 101:e32125. [PMID: 36482639 PMCID: PMC9726278 DOI: 10.1097/md.0000000000032125] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/10/2022] [Indexed: 12/13/2022] Open
Abstract
Fracture is associated with osteopenia after osteoporosis. Neutrophil-lymphocyte ratio (NLR) is common in inflammatory diseases. NLR can be used as an effective clinical tool to assess postmenopausal osteoporosis. The aim of this study is to further explore the relationship between elevated NLR and the severity of osteoporotic vertebrae fractures and femoral neck fracture based on magnetic resonance imaging (MRI). A total of 80 patients with osteoporotic vertebrae fractures, osteoporotic femoral neck fracture in Baoding Second Central Hospital from 2017 to 2020 were selected as the research objects. This study included a series of pretreatment factors, mainly including white blood cell count, red blood cell count, hemoglobin, and the general condition of the patients. Statistical methods included Pearson chi-square test, Spearman correlation test, logistic regression analysis and receiver operator characteristic (ROC) curve. According to Pearson chi-square test, Spearman correlation test, univariate/multivariate logistic regression analysis, the severity of osteoporotic vertebrae fractures, osteoporotic femoral neck fracture was significantly correlated with NLR (P < .001). NLR (odds ratio [OR] = 13.229, 95% CI: 4.167-41.998, P < .001) was a significant independent risk factor for osteoporotic vertebrae fractures, osteoporotic femoral neck fracture. receiver operator characteristic (ROC) curve was used to detect the specificity and sensitivity. The level of NLR has an important influence on the severity of osteoporotic vertebrae fractures and femoral neck fracture. The higher the level of NLR, the more serious the osteoporotic vertebrae fractures and femoral neck fracture.
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Affiliation(s)
- Hao Zhu
- Department of Orthopedics, The Second Central Hospital of Baoding, Zhuozhou City, Hebei Province, PR China
| | - Zheng Li
- Department of Orthopedics, The Second Central Hospital of Baoding, Zhuozhou City, Hebei Province, PR China
| | - Yizhai Zhou
- Department of Orthopedics, The Second Central Hospital of Baoding, Zhuozhou City, Hebei Province, PR China
| | - Rugeng Zheng
- Department of Orthopedics, The Second Central Hospital of Baoding, Zhuozhou City, Hebei Province, PR China
| | - Cong Diao
- Obstetrics Department, The Second Central Hospital of Baoding, Zhuozhou City, Hebei Province, PR China
| | - Kepeng Li
- Department of Orthopedics, The Second Central Hospital of Baoding, Zhuozhou City, Hebei Province, PR China
| | - Qi Feng
- Department of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, PR China
| | - Donglai Wang
- Department of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, PR China
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Zhou Y, Qin Y, Mu T, Zheng H, Cai J. Magnetic Resonance Imaging Findings of Intraspinal Tuberculoma in Children. Front Neurol 2022; 13:936837. [PMID: 35983432 PMCID: PMC9378988 DOI: 10.3389/fneur.2022.936837] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022] Open
Abstract
Background and PurposeIntraspinal tuberculoma is a rare disease in children, and its imaging findings have been described in only a few case reports. This study aimed to investigate the magnetic resonance imaging (MRI) features of pediatric intraspinal tuberculoma and to explore the possible pathogenesis of the disease.Materials and MethodsThe clinical and MRI data of 24 child patients with intraspinal tuberculoma (such as 6 cases of intramedullary tuberculoma, 8 cases of intradural extramedullary tuberculoma, and 10 cases of epidural tuberculoma) were retrospectively analyzed. All patients underwent plain and contrast-enhanced MR scans. The diagnosis was confirmed by surgical pathology or by antituberculous treatment and follow-up data.ResultsIntramedullary tuberculoma had a round shape, while intradural extramedullary tuberculoma and epidural tuberculoma presented long-fusiform or en plaque shapes. Regarding MRI signals, intramedullary tuberculoma and extramedullary tuberculoma were mainly isointense on T1-weighted imaging (T1WI) and hypointense or isointense on T2WI. Rim enhancement was observed in intramedullary tuberculoma, and marked homogeneous enhancement was dominant in extramedullary tuberculoma. Ten (10/24) tuberculomas occurred during antituberculous therapy, with intradural extramedullary tuberculoma accounting for 7 cases (7/8), which was significantly more frequent than intramedullary tuberculoma (1/6) or epidural tuberculoma (2/10).ConclusionMRI is important in the diagnosis of intraspinal tuberculoma, which is characterized by isointensity on T1WI, isointensity, or hypointensity on T2WI, and rim or obvious homogeneous enhancement. Some intraspinal tuberculomas, especially intradural extramedullary tuberculomas, might be associated with the “paradoxical response” mechanism during the tuberculosis treatment.
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Affiliation(s)
- Yirui Zhou
- Department of Radiology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yong Qin
- Department of Radiology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Tong Mu
- Department of Radiology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
- Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Helin Zheng
- Department of Radiology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jinhua Cai
- Department of Radiology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Jinhua Cai
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