Skip to main content

Advertisement

SpringerLink
Log in

Prediction of new cerebral ischemic lesion after carotid artery stenting: a high-resolution vessel wall MRI-based radiomics analysis

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Carotid artery stenting (CAS) is an established treatment for local stenosis. The most common complication is new ipsilateral ischemic lesions (NIILs). This study aimed to develop models considering lesion morphological and compositional features, and radiomics to predict NIILs.

Materials and methods

One hundred and forty-six patients who underwent brain MRI and high-resolution vessel wall MR imaging (hrVWI) before and after CAS were retrospectively recruited. Lumen and outer wall boundaries were segmented on hrVWI as well as atherosclerotic components. A traditional model was constructed with patient clinical information, and lesion morphological and compositional features. Least absolute shrinkage and selection operator algorithm was performed to determine key radiomics features for reconstructing a radiomics model. The model in predicting NIILs was trained and its performance was tested.

Results

Sixty-one patients were NIIL-positive and eighty-five negative. Volume percentage of intraplaque hemorrhage (IPH) and patients’ clinical presentation (symptomatic/asymptomatic) were risk factors of NIILs. The traditional model considering these two features achieved an area under the curve (AUC) of 0.778 and 0.777 in the training and test cohorts, respectively. Twenty-two key radiomics features were identified and the model based on these features achieved an AUC of 0.885 and 0.801 in the two cohorts. The AUCs of the combined model considering IPH volume percentage, clinical presentation, and radiomics features were 0.893 and 0.842 in the training and test cohort respectively.

Conclusions

Compared with traditional features (clinical and compositional features), the combination of traditional and radiomics features improved the power in predicting NIILs after CAS.

Key Points

• Volume percentage of IPH and symptomatic events were independent risk factors of new ipsilateral ischemic lesions (NIILs).

• Radiomics features derived from carotid artery high-resolution vessel wall imaging had great potential in predicting NIILs after CAS.

• The combination model with radiomics and traditional features further improved the diagnostic performance than traditional features alone.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2.
Fig. 3.
Fig. 4.

Similar content being viewed by others

Abbreviations

AUC :

Area under the curve

CAS:

Carotid artery stenting

CEA:

Carotid endarterectomy

CI:

Confidence interval

DWI :

Diffusion-weighted imaging

hrVWI:

High-resolution vessel wall imaging

IPH:

Intraplaque hemorrhage

LASSO:

Least absolute shrinkage and selection operator

LoG:

Laplacian of Gaussian

MRI:

Magnetic resonance imaging

NIILs:

New ipsilateral ischemic lesions

OR:

Odds ratio

TOF:

Time of flight

References

  1. Ooi YC, Gonzalez NR (2015) Management of extracranial carotid artery disease. Cardiol Clin 33:1–35

    Article  PubMed  PubMed Central  Google Scholar 

  2. Brott TG, Howard G, Roubin GS et al (2016) Long-term results of stenting versus endarterectomy for carotid-artery stenosis. N Engl J Med 374:1021–1031

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Yang B, Ma Y, Wang T et al (2021) Carotid endarterectomy and stenting in a Chinese population: safety outcome of the revascularization of extracranial carotid artery stenosis trial. Transl Stroke Res 12:239–247

    Article  PubMed  Google Scholar 

  4. Lacroix V, Hammer F, Astarci P et al (2007) Ischemic cerebral lesions after carotid surgery and carotid stenting. Eur J Vasc Endovasc Surg 33:430–435

    Article  CAS  PubMed  Google Scholar 

  5. Mantese VA, Timaran CH, Chiu D, Begg RJ, Brott TG (2010) The Carotid Revascularization Endarterectomy versus Stenting Trial (CREST): stenting versus carotid endarterectomy for carotid disease. Stroke 41:S31–S34

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gensicke H, van der Worp HB, Nederkoorn PJ et al (2015) Ischemic brain lesions after carotid artery stenting increase future cerebrovascular risk. J Am Coll Cardiol 65:521–529

    Article  PubMed  PubMed Central  Google Scholar 

  7. Zhou W, Baughman BD, Soman S et al (2017) Volume of subclinical embolic infarct correlates to long-term cognitive changes after carotid revascularization. J Vasc Surg 65:686–694

    Article  PubMed  Google Scholar 

  8. Topol EJ, Yadav JS (2000) Recognition of the importance of embolization in atherosclerotic vascular disease. Circulation 101:570–580

    Article  CAS  PubMed  Google Scholar 

  9. Kerwin WS, Miller Z, Yuan C (2017) Imaging of the high-risk carotid plaque: magnetic resonance imaging. Semin Vasc Surg 30:54–61

    Article  PubMed  Google Scholar 

  10. Maekawa K, Shibata M, Nakajima H et al (2018) Cholesterol crystals in embolic debris are associated with postoperative cerebral embolism after carotid artery stenting. Cerebrovasc Dis 46:242–248

    Article  PubMed  Google Scholar 

  11. Nakagawa I, Kotsugi M, Park HS et al (2021) Near-infrared spectroscopy carotid plaque characteristics and cerebral embolism in carotid artery stenting. EuroIntervention 17:599–606

    Article  PubMed  PubMed Central  Google Scholar 

  12. Yoshimura S, Yamada K, Kawasaki M et al (2011) High-intensity signal on time-of-flight magnetic resonance angiography indicates carotid plaques at high risk for cerebral embolism during stenting. Stroke 42:3132–3137

    Article  PubMed  Google Scholar 

  13. Ji A, Lv P, Dai Y et al (2019) Associations between carotid intraplaque hemorrhage and new ipsilateral ischemic lesions after carotid artery stenting: a quantitative study with conventional multi-contrast MRI. Int J Card Imaging 35:1047–1054

    Article  Google Scholar 

  14. Zhao G, Tang X, Tang H et al (2020) Recent intraplaque hemorrhage is associated with a higher risk of ipsilateral cerebral embolism during carotid artery stenting. World Neurosurg 137:e298–e307

    Article  PubMed  Google Scholar 

  15. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  16. Shi Z, Zhu C, Degnan AJ et al (2018) Identification of high-risk plaque features in intracranial atherosclerosis: initial experience using a radiomic approach. Eur Radiol 28:3912–3921

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zhang R, Zhang Q, Ji A et al (2021) Identification of high-risk carotid plaque with MRI-based radiomics and machine learning. Eur Radiol 31:3116–3126

    Article  PubMed  Google Scholar 

  18. Chen S, Liu C, Chen X, Liu WV, Ma L, Zha Y (2022) A radiomics approach to assess high risk carotid plaques: a non-invasive imaging biomarker, retrospective study. Front Neurol 13:788652

    Article  PubMed  PubMed Central  Google Scholar 

  19. 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 325:445–453

    Article  CAS  PubMed  Google Scholar 

  20. Brott TG, Halperin JL, Abbara S et al (2011) 2011 ASA/ACCF/AHA/AANN/AANS/ACR/ASNR/CNS/SAIP/SCAI/SIR/SNIS/SVM/SVS guideline on the management of patients with extracranial carotid and vertebral artery disease: executive summary. J Neurointerv Surg 3:100–130

    Article  PubMed  Google Scholar 

  21. Chu B, Zhao XQ, Saam T et al (2005) Feasibility of in vivo, multicontrast-weighted MR imaging of carotid atherosclerosis for multicenter studies. J Magn Reson Imaging 21:809–817

    Article  PubMed  Google Scholar 

  22. Saam T, Ferguson MS, Yarnykh VL et al (2005) Quantitative evaluation of carotid plaque composition by in vivo MRI. Arterioscler Thromb Vasc Biol 25:234–239

    Article  CAS  PubMed  Google Scholar 

  23. Porambo ME, DeMarco JK (2020) MR imaging of vulnerable carotid plaque. Cardiovasc Diagn Ther 10:1019–1031

    Article  PubMed  PubMed Central  Google Scholar 

  24. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  PubMed  PubMed Central  Google Scholar 

  25. Vickers AJ, van Calster B, Steyerberg EW (2019) A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res 3:18

    Article  PubMed  PubMed Central  Google Scholar 

  26. Rots ML, Meershoek AJA, Bonati LH, den Ruijter HM, de Borst GJ (2019) Editor's Choice - predictors of new ischaemic brain lesions on diffusion weighted imaging after carotid stenting and endarterectomy: a systematic review. Eur J Vasc Endovasc Surg 58:163–174

    Article  PubMed  Google Scholar 

  27. Roh HG, Byun HS, Ryoo JW et al (2005) Prospective analysis of cerebral infarction after carotid endarterectomy and carotid artery stent placement by using diffusion-weighted imaging. AJNR Am J Neuroradiol 26:376–384

    PubMed  PubMed Central  Google Scholar 

  28. Underhill HR, Hatsukami TS, Fayad ZA, Fuster V, Yuan C (2010) MRI of carotid atherosclerosis: clinical implications and future directions. Nat Rev Cardiol 7:165–173

    Article  PubMed  Google Scholar 

  29. Clarke SE, Hammond RR, Mitchell JR, Rutt BK (2003) Quantitative assessment of carotid plaque composition using multicontrast MRI and registered histology. Magn Reson Med 50:1199–1208

    Article  PubMed  Google Scholar 

  30. den Hartog AG, Bovens SM, Koning W et al (2013) Current status of clinical magnetic resonance imaging for plaque characterisation in patients with carotid artery stenosis. Eur J Vasc Endovasc Surg 45:7–21

    Article  Google Scholar 

  31. Zhao G, Tang I, Tang H, Lin J, Xue S, Guo D (2021) Predictors of ipsilateral new ischemic lesions on diffusion-weighted imaging after carotid artery stenting in asymptomatic patients: a retrospective observational study with conventional multicontrast MRI. Ann Vasc Surg 74:95–104

    Article  PubMed  Google Scholar 

  32. Uchiyama N, Misaki K, Mohri M et al (2012) Association between carotid plaque composition assessed by multidetector computed tomography and cerebral embolism after carotid stenting. Neuroradiology 54:487–493

    Article  PubMed  Google Scholar 

  33. Kernan WN, Ovbiagele B, Black HR et al (2014) Guidelines for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 45:2160–2236

    Article  PubMed  Google Scholar 

  34. Kakkos SK, Stevens JM, Nicolaides AN et al (2007) Texture analysis of ultrasonic images of symptomatic carotid plaques can identify those plaques associated with ipsilateral embolic brain infarction. Eur J Vasc Endovasc Surg 33:422–429

    Article  CAS  PubMed  Google Scholar 

  35. Jamthikar A, Gupta D, Khanna NN, Saba L, Laird JR, Suri JS (2020) Cardiovascular/stroke risk prevention: a new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors. Indian Heart J 72:258–264

    Article  PubMed  PubMed Central  Google Scholar 

  36. Cilla S, Macchia G, Lenkowicz J et al (2022) CT angiography-based radiomics as a tool for carotid plaque characterization: a pilot study. Radiol Med. https://doi.org/10.1007/s11547-022-01505-5

  37. Shi Z, Li J, Zhao M et al (2020) Quantitative histogram analysis on intracranial atherosclerotic plaques: a high-resolution magnetic resonance imaging study. Stroke 51:2161–2169

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Qiao Y, Etesami M, Astor BC, Zeiler SR, Trout HH 3rd, Wasserman BA (2012) Carotid plaque neovascularization and hemorrhage detected by MR imaging are associated with recent cerebrovascular ischemic events. AJNR Am J Neuroradiol 33:755–760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This study was supported by the National Key Research, Development Program of China (No. 2018YFC1312301; No. 2018YFC1312900); the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); and the DRAGON project (101005122).

Author information

Authors and Affiliations

  1. Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Shanghai, China

    Ranying Zhang, Aihua Ji, Peng Lv & Jiang Lin

  2. Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, 145 Middle Shandong Road, Shanghai, China

    Qingwei Zhang

  3. Tenoke Ltd., Cambridge, UK

    Julio Acosta- Cabronero

  4. MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China

    Caixia Fu

  5. Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China

    Jing Ding

  6. Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China

    Daqiao Guo

  7. Department of Radiology, University of Cambridge, Cambridge, UK

    Zhongzhao Teng

  8. Nanjing Jingsan Medical Science and Technology, Nanjing, China

    Zhongzhao Teng

Authors
  1. Ranying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingwei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aihua Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Julio Acosta- Cabronero
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Caixia Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jing Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Daqiao Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zhongzhao Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jiang Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhongzhao Teng or Jiang Lin.

Ethics declarations

Institutional review board approval was obtained (Project ID No. B2020-400R by the Ethics Committee of Zhongshan Hospital, Shanghai, China).

Guarantor

The scientific guarantor of this publication is Jiang Lin and Zhongzhao Teng.

Conflict of interest

Dr. Teng is the chief scientist of Tenoke Ltd., Cambridge and Nanjing. Jingsan Medical Science and Technology, Ltd., China. Other authors do not any conflict of interest to declare.

Statistics and biometry

Qingwei Zhang and Ranying Zhang contributed to the statistical analysis.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology:

• retrospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ranying Zhang, Qingwei Zhang, and Aihua Ji share equal authorship.

Supplementary information

ESM 1

(DOCX 720 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, R., Zhang, Q., Ji, A. et al. Prediction of new cerebral ischemic lesion after carotid artery stenting: a high-resolution vessel wall MRI-based radiomics analysis. Eur Radiol 33, 4115–4126 (2023). https://doi.org/10.1007/s00330-022-09302-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-022-09302-4

Keywords

Search

Navigation