The value of black-blood MRI in cerebral venous thrombosis diagnosis: A systematic review and meta-analysis

Abstract

Objective

To evaluate the diagnostic performance of black-blood MRI (BB-MRI) in detecting cerebral venous thrombosis (CVT).

Methods

This study was registered in PROSPERO [ID: CRD42024556451]. The PubMed/MEDLINE, Web of Science, Scopus, and Embase databases were searched for studies evaluating the performance of BB-MRI in diagnosing CVT. Studies were selected based on predefined eligibility criteria. The risk of bias was assessed using the QUADAS-2 tool. Meta-analysis was performed using a bivariate random-effects model to calculate pooled sensitivity, specificity, and area under the curve (AUC).

Results

Nine studies fulfilled the eligibility criteria and were included in the review; however, two studies insufficiently reported quantitative data. Thus, seven studies involving 176 CVT patients and 217 controls, encompassing 610 thrombosed and 3279 normal cerebral venous segments, were included in the meta-analysis. The studies demonstrated a high risk of bias in the patient selection and reference standard domains. The pooled sensitivity and specificity of BB-MRI were 96% [95% CI: 92%–98%] and 96% [95% CI: 93%–98%] on a patient-based level, and 92% [95% CI: 87%–95%] and 98% [95% CI: 92%–99%] on a venous-segment-based level, respectively. The AUC was 0.98 for patient-based data and 0.96 for venous-segment-based data.

Conclusions

BB-MRI demonstrates a significant potential as a diagnostic method for CVT. However, further comparative studies are needed to define its role in the diagnostic workup of patients suspected of having CVT.

Keywords

Black-blood MRI vessel wall imaging magnetic resonance direct thrombus imaging cerebral venous thrombosis cerebral cortical venous thrombosis

Get full access to this article

View all access options for this article.

References

Saposnik

Barinagarrementeria

Brown

Jr. , et al. Diagnosis and management of cerebral venous thrombosis: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2011; 42(4): 1158–1192.

Silvis

de Sousa

Ferro

, et al. Cerebral venous thrombosis. Nat Rev Neurol 2017; 13(9): 555–565.

Song

Dornbos

3rd Lan

, et al. High-Resolution magnetic resonance black blood thrombus imaging and serum D-dimer in the confirmation of acute cortical vein thrombosis. Front Neurol 2021; 12: 680040.

Yang

Duan

Fan

, et al. Early detection and quantification of cerebral venous thrombosis by magnetic resonance black-blood thrombus imaging. Stroke 2016; 47(2): 404–409.

Moody

. Magnetic resonance direct thrombus imaging. J Thromb Haemost 2003; 1(7): 1403–1409.

Kim

Park

Nahrendorf

, et al. Direct thrombus imaging in stroke. J Stroke 2016; 18(3): 286–296.

Page

Moher

Bossuyt

, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 2021; 372: n160.

Deeks

Bossuyt

Leeflang

, et al. Cochrane handbook for systematic reviews of diagnostic test accuracy. Chichester (UK): John Wiley & Sons, 2023.

Whiting

Rutjes

Westwood

, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155(8): 529–536.

10.

Reitsma

Glas

Rutjes

, et al. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 2005; 58(10): 982–990.

11.

Lee

Kim

Choi

, et al. Systematic review and meta-analysis of studies evaluating diagnostic test accuracy: a practical review for clinical researchers-Part II. Statistical methods of meta-analysis. Korean J Radiol 2015; 16(6): 1188–1196.

12.

Holling

Böhning

Masoudi

, et al. Evaluation of a new version of I² with emphasis on diagnostic problems. Commun Stat Simul Comput 2020; 49(4): 942–972.

13.

R Core Team . _R: a language and environment for statistical computing_. Vienna, Austria: R Foundation for Statistical Computing, 2024. https://www.R-project.org/

14.

Sousa-Pinto PDwcfB . _mada: meta-analysis of diagnostic accuracy_. R package version 0.5.11 2022. https://CRAN.R-project.org/package=mada

15.

Niu

Guo

, et al. Diagnosis of non-acute cerebral venous thrombosis with 3D T1-weighted black blood sequence at 3T. J Neurol Sci 2016; 367: 46–50.

16.

Wang

Yang

Duan

, et al. Cerebral venous thrombosis: MR black-blood thrombus imaging with enhanced blood signal suppression. AJNR Am J Neuroradiol 2019; 40(10): 1725–1730.

17.

Yang

Liu

, et al. Diagnostic performance of MR black-blood thrombus imaging for cerebral venous thrombosis in real-world clinical practice. Eur Radiol 2022; 32(3): 2041–2049.

18.

Hakim

Kurmann

Pospieszny

, et al. Diagnostic accuracy of high-resolution 3D T2-SPACE in detecting cerebral venous sinus thrombosis. AJNR Am J Neuroradiol 2022; 43(6): 881–886.

19.

Ahmed

Arora

Kulanthaivelu

, et al. Utility of 3D T1-weighted turbo spin echo black blood sequence for the diagnosis of cerebral venous thrombosis. Emerg Radiol 2023; 30(4): 443–451.

20.

Xiong

Liu

Zhang

, et al. Identification and quantitative evaluation of symptomatic cerebral venous thrombosis using 3D variable flip angle turbo spin echo. Magn Reson Imaging 2023; 103: 131–138.

21.

Tunlayadechanont

Chobaroon

Chansakul

. Contrast-enhanced 3D black-blood magnetic resonance imaging for diagnosis of cerebral venous thrombosis. Neuroradiol J 2024; 37: 738–743.

22.

van Dam

van Walderveen

MAA

Kroft

LJM

, et al. Current imaging modalities for diagnosing cerebral vein thrombosis - a critical review. Thromb Res 2020; 189: 132–139.

23.

Klingebiel

Bauknecht

Bohner

, et al. Comparative evaluation of 2D time-of-flight and 3D elliptic centric contrast-enhanced MR venography in patients with presumptive cerebral venous and sinus thrombosis. Eur J Neurol 2007; 14(2): 139–143.

24.

Lai

Hsiao

, et al. Comparison of real-time three-dimensional gadolinium-enhanced elliptic centric-ordered MR venography and two-dimensional time-of-flight MR venography of the intracranial venous system. J Chin Med Assoc 2010; 73(3): 131–138.

25.

Pui

. Cerebral MR venography. Clin Imaging 2004; 28(2): 85–89.

26.

Poon

Chang

Swarnkar

, et al. Radiologic diagnosis of cerebral venous thrombosis: pictorial review. AJR Am J Roentgenol 2007; 189(6 Suppl): S64–75.

27.

Idbaih

Boukobza

Crassard

, et al. MRI of clot in cerebral venous thrombosis: high diagnostic value of susceptibility-weighted images. Stroke 2006; 37(4): 991–995.

28.

Boukerche

Balakrishnan

Kalapos

, et al. High-resolution susceptibility-weighted imaging of clots in cerebral venous thrombosis. Neuroradiology 2022; 64(12): 2267–2275.

29.

Boukerche

Balakrishnan

Kalapos

, et al. Detection of cerebral cortical vein thrombosis with high-resolution susceptibility weighted imaging - a comparison with MR venography and standard MR sequences. Neuroradiology 2023; 65(5): 885–892.

30.

Boukobza

Guinebert

Laissy

. Comparison of susceptibility-weighted angiography (SWAN) and T2 gradient-echo sequences for the detection of acute cerebral venous thrombosis. Neuroradiol J 2023; 36(2): 148–157.

31.

Cohen

Korevaar

Altman

, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open 2016; 6(11): e012799.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.08 MB