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Diagnostic performance of MRI for detecting intraplaque hemorrhage in the carotid arteries: a meta-analysis

  • Tao Zhou
  • Shouqiang Jia
  • Xiu Wang
  • Bin Wang
  • Zhiguo Wang
  • Ting Wu
  • Ying Li
  • Ying Chen
  • Chenxiao Yang
  • Qingguo Li
  • Zhen Yang
  • Min LiEmail author
  • Gang SunEmail author
Magnetic Resonance
  • 93 Downloads

Abstract

Objectives

To investigate the diagnostic performance of MRI in diagnosing carotid atherosclerotic intraplaque hemorrhage (IPH) and to provide a clinical guide for MRI application.

Methods

We searched MEDLINE, Embase, and Cochrane library from the earliest available date of indexing through November 30, 2017. All investigators screened and selected studies comparing the use of MRI with histology. The accuracy to diagnose pathological IPH was expressed by sensitivity, specificity, negative likelihood ratios (LRs), positive LRs, and the area under summary receiver-operating characteristic (SROC) curve. We calculated the post-test probability to assess the clinical utility of MRI.

Results

We analyzed 696 patients from 20 articles. The sensitivity and specificity were 87% (95% CI, 81–91%) and 92% (95% CI, 87–95%), respectively. The positive and negative LRs were 10.27 (95% CI, 6.76–15.59) and 0.15 (95% CI, 0.10–0.21), respectively. The area under SROC curve was 0.95 (95% CI, 0.93–0.97). MRI was accurate in confirming or in ruling out disease over a wide range of pre-test probabilities of IPH: MRI could increase the post-test probability to > 80% in patients with a pre-test probability > 27% and could decrease the post-test probability to < 20% in patients with a pre-test probability < 64%.

Conclusion

Non-invasive MRI has excellent specificity and good sensitivity for diagnosing IPH. MRI is a tool for confirming or ruling out carotid atherosclerotic IPH.

Key Points

• Non-invasive MRI has excellent performance for diagnosing IPH, which is a component of vulnerable plaque.

• The high accuracy of MRI for IPH helps clinicians analyze the prognosis of clinical events and plan personalized treatment.

Keywords

Carotid artery plaque Hemorrhage Stroke Magnetic resonance imaging 

Abbreviations

AUROC

Area under receiver of operating characteristic

CE

Contrast enhanced

CI

Confidence interval

DTI

Direct thrombus imaging

FFE

Fast field echo

FSE

Fast-spin echo

GRE

Gradient recalled echo

IPH

Intraplaque hemorrhage

LR

Likelihood ratio

MRA

MR angiography

MRI

Magnetic resonance imaging

PDWI

Proton density weighted imaging

QUADAS

Quality Assessment of Diagnostic Accuracy Studies

RAGE

Rapid acquisition gradient echo

SE

Spin echo

SROC

Summary receiver-operating characteristic

T1WI

T1-weighted imaging

T2WI

T2-weighted imaging

TFE

Turbo field echo

TOF

Time of flight

TSE

Turbo spin echo

Notes

Funding

This study has received funding by grants from the National Key R&D Program of China (2016YFC1300300).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Gang Sun.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

The author Min Li has significant statistical expertise.

Informed consent

Written informed consent was not required for this study because all analyses were based on previously published studies; thus, no patient consent is required.

Ethical approval

Institutional Review Board approval was not required because all analyses were based on previously published studies; thus, no ethical approval is required.

Methodology

• prospective

• diagnostic study

• multicenter study

Supplementary material

330_2019_6053_MOESM1_ESM.docx (422 kb)
ESM 1 (DOCX 421 kb)

References

  1. 1.
    Howard DP, van Lammeren GW, Rothwell PM et al (2015) Symptomatic carotid atherosclerotic disease: correlations between plaque composition and ipsilateral stroke risk. Stroke 46:182–189CrossRefGoogle Scholar
  2. 2.
    Park JS, Kwak HS, Lee JM, Koh EJ, Chung GH, Hwang SB (2015) Association of carotid intraplaque hemorrhage and territorial acute infarction in patients with acute neurological symptoms using carotid magnetization-prepared rapid acquisition with gradient-echo. J Korean Neurosurg Soc 57:94–99CrossRefGoogle Scholar
  3. 3.
    Halliday A, Harrison M, Hayter E et al (2010) 10-year stroke prevention after successful carotid endarterectomy for asymptomatic stenosis (ACST-1): a multicentre randomised trial. Lancet 376:1074–1084CrossRefGoogle Scholar
  4. 4.
    Barnett HJM, Taylor DW, Haynes RB et al (1991) Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. N Engl J Med 325:445–453CrossRefGoogle Scholar
  5. 5.
    Brinjikji W, Huston J 3rd, Rabinstein AA, Kim GM, Lerman A, Lanzino G (2016) Contemporary carotid imaging: from degree of stenosis to plaque vulnerability. J Neurosurg 124:27–42CrossRefGoogle Scholar
  6. 6.
    Freilinger TM, Schindler A, Schmidt C et al (2012) Prevalence of nonstenosing, complicated atherosclerotic plaques in cryptogenic stroke. JACC Cardiovasc Imaging 5:397–405CrossRefGoogle Scholar
  7. 7.
    Kolodgie FD, Yahagi K, Mori H et al (2017) High-risk carotid plaque: lessons learned from histopathology. Semin Vasc Surg 30:31–43CrossRefGoogle Scholar
  8. 8.
    Zhao Q, Zhao X, Cai Z, Li F, Yuan C, Cai J (2011) Correlation of coronary plaque phenotype and carotid atherosclerotic plaque composition. Am J Med Sci 342:480–485CrossRefGoogle Scholar
  9. 9.
    McNally JS, McLaughlin MS, Hinckley PJ et al (2015) Intraluminal thrombus, intraplaque hemorrhage, plaque thickness, and current smoking optimally predict carotid stroke. Stroke 46:84–90CrossRefGoogle Scholar
  10. 10.
    Fisher M, Paganini-Hill A, Martin A et al (2005) Carotid plaque pathology: thrombosis, ulceration, and stroke pathogenesis. Stroke 36:253–257CrossRefGoogle Scholar
  11. 11.
    Stary HC (2000) Natural history and histological classification of atherosclerotic lesions: an update. Arterioscler Thromb Vasc Biol 20:1177–1178CrossRefGoogle Scholar
  12. 12.
    Ramnarine KV, Garrard JW, Kanber B, Nduwayo S, Hartshorne TC, Robinson TG (2014) Shear wave elastography imaging of carotid plaques: feasible, reproducible and of clinical potential. Cardiovasc Ultrasound 12:49CrossRefGoogle Scholar
  13. 13.
    Kanber B, Hartshorne TC, Horsfield MA, Naylor AR, Robinson TG, Ramnarine KV (2015) A novel ultrasound-based carotid plaque risk index associated with the presence of cerebrovascular symptoms. Ultraschall Med 36:480–486Google Scholar
  14. 14.
    Kwee RM, van Oostenbrugge RJ, Hofstra L et al (2008) Identifying vulnerable carotid plaques by noninvasive imaging. Neurology 70:2401–2409CrossRefGoogle Scholar
  15. 15.
    Arai D, Yamaguchi S, Murakami M et al (2011) Characteristics of carotid plaque findings on ultrasonography and black blood magnetic resonance imaging in comparison with pathological findings. Acta Neurochir Suppl 112:15–19CrossRefGoogle Scholar
  16. 16.
    Shimada Y, Oikawa K, Fujiwara S et al (2017) Comparison of three-dimensional T1-weighted magnetic resonance and contrast-enhanced ultrasound plaque images for severe stenosis of the cervical carotid artery. J Stroke Cerebrovasc Dis 26:1916–1922CrossRefGoogle Scholar
  17. 17.
    Rafailidis V, Chryssogonidis I, Xerras C et al (2018) A comparative study of color Doppler imaging and contrast-enhanced ultrasound for the detection of ulceration in patients with carotid atherosclerotic disease. Eur Radiol.  https://doi.org/10.1007/s00330-018-5773-8
  18. 18.
    Yao B, Yang L, Wang G et al (2016) Diffusion measurement of intraplaque hemorrhage and intramural hematoma using diffusion weighted MRI at 3T in cervical artery. Eur Radiol 26:3737–3743CrossRefGoogle Scholar
  19. 19.
    Chai JT, Biasiolli L, Li L et al (2016) Quantification of lipid-rich core in carotid atherosclerosis using magnetic resonance T2 mapping: relation to clinical presentation. JACC Cardiovasc Imaging 10:747–756CrossRefGoogle Scholar
  20. 20.
    Narumi S, Sasaki M, Natori T et al (2015) Carotid plaque characterization using 3D T1-weighted MR imaging with histopathologic validation: a comparison with 2D technique. AJNR Am J Neuroradiol 36:751–756CrossRefGoogle Scholar
  21. 21.
    Whiting P, Rutjes AW, Reitsma JB, Bossuyt PM, Kleijnen J (2003) The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol 3:25CrossRefGoogle Scholar
  22. 22.
    Deeks JJ, Macaskill P, Irwig L (2005) The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 58:882–893CrossRefGoogle Scholar
  23. 23.
    Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58:982–990CrossRefGoogle Scholar
  24. 24.
    Chu H, Cole SR (2006) Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol 59:1331–1332 author reply 1332-1333CrossRefGoogle Scholar
  25. 25.
    Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557–560CrossRefGoogle Scholar
  26. 26.
    Jaeschke R, Guyatt GH, Sackett DL (1994) Users’ guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. JAMA 271:703–707CrossRefGoogle Scholar
  27. 27.
    Lukanova DV, Nikolov NK, Genova KZ, Stankev MD, Georgieva EV (2015) The accuracy of noninvasive imaging techniques in diagnosis of carotid plaque morphology. Open Access Maced J Med Sci 3:224–230CrossRefGoogle Scholar
  28. 28.
    Millon A, Mathevet JL, Boussel L et al (2013) High-resolution magnetic resonance imaging of carotid atherosclerosis identifies vulnerable carotid plaques. J Vasc Surg 57:1046–1051.e2CrossRefGoogle Scholar
  29. 29.
    Narumi S, Sasaki M, Ohba H et al (2013) Prediction of carotid plaque characteristics using non-gated MR imaging: correlation with endarterectomy specimens. AJNR Am J Neuroradiol 34:191–197CrossRefGoogle Scholar
  30. 30.
    Qiao Y, Etesami M, Malhotra S et al (2011) Identification of intraplaque hemorrhage on MR angiography images: a comparison of contrast-enhanced mask and time-of-flight techniques. AJNR Am J Neuroradiol 32:454–459CrossRefGoogle Scholar
  31. 31.
    Ota H, Yarnykh VL, Ferguson MS et al (2010) Carotid intraplaque hemorrhage imaging at 3.0-T MR imaging: comparison of the diagnostic performance of three T1-weighted sequences. Radiology 254:551–563CrossRefGoogle Scholar
  32. 32.
    Yim YJ, Choe YH, Ko Y et al (2008) High signal intensity halo around the carotid artery on maximum intensity projection images of time-of-flight MR angiography: a new sign for intraplaque hemorrhage. J Magn Reson Imaging 27:1341–1346CrossRefGoogle Scholar
  33. 33.
    Watanabe Y, Nagayama M, Suga T et al (2008) Characterization of atherosclerotic plaque of carotid arteries with histopathological correlation: vascular wall MR imaging vs. color Doppler ultrasonography (US). J Magn Reson Imaging 28:478–485CrossRefGoogle Scholar
  34. 34.
    Bitar R, Moody AR, Leung G et al (2008) In vivo 3D high-spatial-resolution MR imaging of intraplaque hemorrhage. Radiology 249:259–267CrossRefGoogle Scholar
  35. 35.
    Esposito L, Sievers M, Sander D et al (2007) Detection of unstable carotid artery stenosis using MRI. J Neurol 254:1714–1722CrossRefGoogle Scholar
  36. 36.
    Puppini G, Furlan F, Cirota N et al (2006) Characterisation of carotid atherosclerotic plaque: comparison between magnetic resonance imaging and histology. Radiol Med 111:921–930CrossRefGoogle Scholar
  37. 37.
    Honda M, Kitagawa N, Tsutsumi K, Nagata I, Morikawa M, Hayashi T (2006) High-resolution magnetic resonance imaging for detection of carotid plaques. Neurosurgery 58:338–346 discussion 338-346CrossRefGoogle Scholar
  38. 38.
    Clarke SE, Beletsky V, Hammond RR, Hegele RA, Rutt BK (2006) Validation of automatically classified magnetic resonance images for carotid plaque compositional analysis. Stroke 37:93–97CrossRefGoogle Scholar
  39. 39.
    Saam T, Ferguson MS, Yarnykh VL et al (2005) Quantitative evaluation of carotid plaque composition by in vivo MRI. Arterioscler Thromb Vasc Biol 25:234–239CrossRefGoogle Scholar
  40. 40.
    Kampschulte A, Ferguson MS, Kerwin WS et al (2004) Differentiation of intraplaque versus juxtaluminal hemorrhage/thrombus in advanced human carotid atherosclerotic lesions by in vivo magnetic resonance imaging. Circulation 110:3239–3244CrossRefGoogle Scholar
  41. 41.
    Chu B, Kampschulte A, Ferguson MS et al (2004) Hemorrhage in the atherosclerotic carotid plaque: a high-resolution MRI study. Stroke 35:1079–1084CrossRefGoogle Scholar
  42. 42.
    Cappendijk VC, Cleutjens KB, Heeneman S et al (2004) In vivo detection of hemorrhage in human atherosclerotic plaques with magnetic resonance imaging. J Magn Reson Imaging 20:105–110CrossRefGoogle Scholar
  43. 43.
    Moody AR, Murphy RE, Morgan PS et al (2003) Characterization of complicated carotid plaque with magnetic resonance direct thrombus imaging in patients with cerebral ischemia. Circulation 107:3047–3052CrossRefGoogle Scholar
  44. 44.
    Cai JM, Hatsukami TS, Ferguson MS, Small R, Polissar NL, Yuan C (2002) Classification of human carotid atherosclerotic lesions with in vivo multicontrast magnetic resonance imaging. Circulation 106:1368–1373CrossRefGoogle Scholar
  45. 45.
    Wang X, Sun J, Zhao X et al (2017) Ipsilateral plaques display higher T1 signals than contralateral plaques in recently symptomatic patients with bilateral carotid intraplaque hemorrhage. Atherosclerosis 257:78–85CrossRefGoogle Scholar
  46. 46.
    Sun J, Underhill HR, Hippe DS, Xue Y, Yuan C, Hatsukami TS (2012) Sustained acceleration in carotid atherosclerotic plaque progression with intraplaque hemorrhage: a long-term time course study. JACC Cardiovasc Imaging 5:798–804CrossRefGoogle Scholar
  47. 47.
    Raman SV, Winner MW 3rd, Tran T et al (2008) In vivo atherosclerotic plaque characterization using magnetic susceptibility distinguishes symptom-producing plaques. JACC Cardiovasc Imaging 1:49–57CrossRefGoogle Scholar
  48. 48.
    Saam T, Hetterich H, Hoffmann V et al (2013) Meta-analysis and systematic review of the predictive value of carotid plaque hemorrhage on cerebrovascular events by magnetic resonance imaging. J Am Coll Cardiol 62:1081–1091CrossRefGoogle Scholar
  49. 49.
    Li D, Zhao H, Chen X et al (2018) Identification of intraplaque haemorrhage in carotid artery by simultaneous non-contrast angiography and intraPlaque haemorrhage (SNAP) imaging: a magnetic resonance vessel wall imaging study. Eur Radiol 28:1681–1686CrossRefGoogle Scholar
  50. 50.
    Oei ML, Ozgun M, Seifarth H et al (2010) T1-weighted MRI for the detection of coronary artery plaque haemorrhage. Eur Radiol 20:2817–2823CrossRefGoogle Scholar
  51. 51.
    Boyko EJ (1994) Ruling out or ruling in disease with the most sensitive or specific diagnostic test: short cut or wrong turn? Med Decis Making 14:175–179CrossRefGoogle Scholar
  52. 52.
    Yamada N, Higashi M, Otsubo R et al (2007) Association between signal hyperintensity on T1-weighted MR imaging of carotid plaques and ipsilateral ischemic events. AJNR Am J Neuroradiol 28:287–292Google Scholar
  53. 53.
    Yuan C, Mitsumori LM, Ferguson MS et al (2001) In vivo accuracy of multispectral magnetic resonance imaging for identifying lipid-rich necrotic cores and intraplaque hemorrhage in advanced human carotid plaques. Circulation 104:2051–2056CrossRefGoogle Scholar
  54. 54.
    Sun J, Zhao XQ, Balu N et al (2017) Carotid plaque lipid content and fibrous cap status predict systemic CV outcomes: the MRI substudy in AIM-HIGH. JACC Cardiovasc Imaging 10:241–249CrossRefGoogle Scholar
  55. 55.
    Gupta A, Baradaran H, Schweitzer AD et al (2013) Carotid plaque MRI and stroke risk: a systematic review and meta-analysis. Stroke 44:3071–3077CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologyLaiwu Affiliated Hospital of Taishan Medical UniversityLaiwuChina
  2. 2.Department of ICULaiwu Affiliated Hospital of Taishan Medical UniversityLaiwuChina
  3. 3.Department of Health CareShandong University Affiliated Jinan Center HospitalJinanChina
  4. 4.Department of Medical Imaging, 960 Hospital of PLAJinanChina

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