Advertisement

Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning

  • Aglae Velasco GonzalezEmail author
  • Boris Buerke
  • Dennis Görlich
  • Manfred Fobker
  • Thilo Rusche
  • Cristina Sauerland
  • Norbert Meier
  • Astrid Jeibmann
  • Ray McCarthy
  • Harald Kugel
  • Peter Sporns
  • Andreas Faldum
  • Werner Paulus
  • Walter Heindel
Original Article

Abstract

Exact histological clot composition remains unknown. The purpose of this study was to identify the best imaging variables to be extrapolated on clot composition and clarify variability in the imaging of thrombi by non-contrast CT. Using a CT-phantom and covering a wide range of histologies, we analyzed 80 clot analogs with respect to X-ray attenuation at 24 and 48 h after production. The mean, maximum, and minimum HU values for the axial and coronal reconstructions were recorded. Each thrombus underwent a corresponding histological analysis, together with a laboratory analysis of water and iron contents. Decision trees, a type of supervised machine learning, were used to select the primary variable altering attenuation and the best parameter for predicting histology. The decision trees selected red blood cells (RBCs) for correlation with all attenuation parameters (p < 0.001). Conversely, maximum attenuation on axial CT offered the greatest accuracy for discriminating up to four groups of clot histology (p < 0.001). Similar RBC-rich thrombi displayed variable imaging associated with different iron (p = 0.023) and white blood cell contents (p = 0.019). Water content varied among the different histologies but did not in itself account for the differences in attenuation. Independent factors determining clot attenuation were the RBCs (β = 0.33, CI = 0.219–0.441, p < 0.001) followed by the iron content (β = 0.005, CI = 0.0002–0.009, p = 0.042). Our findings suggest that it is possible to extract more and valuable information from NCCT that can be extrapolated to provide insights into clot histological and chemical composition.

Keywords

Blood clot Helical CT Red blood cells Decision trees Iron 

Abbreviations

NCCT

Non-contrast CT

RBCs

Red blood cells

HU

Hounsfield units

ROI

Region of interest

WBCs

White blood cells

SD

Standard deviation

IQR

Interquartile range

AIS

Acute ischemic stroke

Notes

Acknowledgments

The authors thank the team headed by Dr. Ray McCarthy for providing clot analogs weekly as the source material for our study free of charge. Aglaé Velasco Gonzalez (Neuroradiology) performed this study in collaboration with the Institute of Biostatistics and Clinical Research and Institute of Neuropathology at the Faculty of Medicine, Westfälische Wilhelms-Universität Münster (WWU). We also thank Dr. Senner Volker for coordinating and Mrs. Andrea Rothaus of the neuropathology laboratory for preparing and staining the clot analog samples. Special thanks go to our X-ray technicians who always found a way to complete the experiments on time and whose support was invaluable. Finally, the authors thank the University of Muenster for giving us the time to complete this project.

Compliance with Ethical Standards

Conflict of Interest

Aglaé Velasco González performed this study in the context of a program for research backed by the WWU University. One of the authors (Ray McCarthy) is an employee of Cerenovus. Authors who neither advise nor work for the industry had exclusive control over designing and performing the experiment, the data, and data analysis. This study received no industry financial support. All authors have approved the final manuscript.

Supplementary material

12975_2019_766_MOESM1_ESM.docx (2.3 mb)
ESM 1 (DOCX 2356 kb)

References

  1. 1.
    Campbell BC, Mitchell PJ, Kleinig TJ, Dewey HM, Churilov L, Yassi N, et al. Endovascular therapy for ischemic stroke with perfusion-imaging selection. N Engl J Med. 2015;372(11):1009–18.CrossRefGoogle Scholar
  2. 2.
    Goyal M, Demchuk AM, Menon BK, Eesa M, Rempel JL, Thornton J, et al. Randomized assessment of rapid endovascular treatment of ischemic stroke. N Engl J Med. 2015;372(11):1019–30.CrossRefGoogle Scholar
  3. 3.
    Berkhemer OA, Fransen PS, Beumer D, van den Berg LA, Lingsma HF, Yoo AJ, et al. A randomized trial of intraarterial treatment for acute ischemic stroke. N Engl J Med. 2015;372(1):11–20.CrossRefGoogle Scholar
  4. 4.
    Jovin TG, Chamorro A, Cobo E, de Miquel MA, Molina CA, Rovira A, et al. Thrombectomy within 8 hours after symptom onset in ischemic stroke. N Engl J Med. 2015;372(24):2296–306.CrossRefGoogle Scholar
  5. 5.
    Velasco Gonzalez A, Buerke B, Gorlich D, Chapot R, Smagge L, Velasco MV, et al. Variability in the decision-making process of acute ischemic stroke in difficult clinical and radiological constellations: analysis based on a cross-sectional interview-administered stroke questionnaire. Eur Radiol. 2019;29(11):6275–84.CrossRefGoogle Scholar
  6. 6.
    Niesten JM, van der Schaaf IC, van der Graaf Y, Kappelle LJ, Biessels GJ, Horsch AD, et al. Predictive value of thrombus attenuation on thin-slice non-contrast CT for persistent occlusion after intravenous thrombolysis. Cerebrovasc Dis. 2014;37(2):116–22.CrossRefGoogle Scholar
  7. 7.
    Puig J, Pedraza S, Demchuk A, Daunis IEJ, Termes H, Blasco G, et al. Quantification of thrombus Hounsfield units on noncontrast CT predicts stroke subtype and early recanalization after intravenous recombinant tissue plasminogen activator. AJNR Am J Neuroradiol. 2012;33(1):90–6.CrossRefGoogle Scholar
  8. 8.
    Brinjikji W, Duffy S, Burrows A, Hacke W, Liebeskind D, Majoie C, et al. Correlation of imaging and histopathology of thrombi in acute ischemic stroke with etiology and outcome: a systematic review. J Neurointerv Surg. 2017;9(6):529–34.CrossRefGoogle Scholar
  9. 9.
    Heo JH, Kim K, Yoo J, Kim YD, Nam HS, Kim EY. Computed tomography-based thrombus imaging for the prediction of recanalization after reperfusion therapy in stroke. J Stroke. 2017;19(1):40–9.CrossRefGoogle Scholar
  10. 10.
    Froehler MT, Tateshima S, Duckwiler G, Jahan R, Gonzalez N, Vinuela F, et al. The hyperdense vessel sign on CT predicts successful recanalization with the Merci device in acute ischemic stroke. J Neurointerv Surg. 2013;5(4):289–93.CrossRefGoogle Scholar
  11. 11.
    Mokin M, Morr S, Natarajan SK, Lin N, Snyder KV, Hopkins LN, et al. Thrombus density predicts successful recanalization with Solitaire stent retriever thrombectomy in acute ischemic stroke. J Neurointerv Surg. 2015;7(2):104–7.CrossRefGoogle Scholar
  12. 12.
    Liebeskind DS, Sanossian N, Yong WH, Starkman S, Tsang MP, Moya AL, et al. CT and MRI early vessel signs reflect clot composition in acute stroke. Stroke. 2011;42(5):1237–43.CrossRefGoogle Scholar
  13. 13.
    Moftakhar P, English JD, Cooke DL, Kim WT, Stout C, Smith WS, et al. Density of thrombus on admission CT predicts revascularization efficacy in large vessel occlusion acute ischemic stroke. Stroke. 2013;44(1):243–5.CrossRefGoogle Scholar
  14. 14.
    Mair G, Boyd EV, Chappell FM, von Kummer R, Lindley RI, Sandercock P, et al. Sensitivity and specificity of the hyperdense artery sign for arterial obstruction in acute ischemic stroke. Stroke. 2015;46(1):102–7.CrossRefGoogle Scholar
  15. 15.
    Brouwer PA, Brinjikji W, De Meyer SF. Clot pathophysiology: why is it clinically important? Neuroimaging Clin N Am. 2018;28(4):611–23.CrossRefGoogle Scholar
  16. 16.
    Turk AS 3rd, Campbell JM, Spiotta A, Vargas J, Turner RD, Chaudry MI, et al. An investigation of the cost and benefit of mechanical thrombectomy for endovascular treatment of acute ischemic stroke. J Neurointerv Surg. 2014;6(1):77–80.CrossRefGoogle Scholar
  17. 17.
    Shu L, Meyne J, Jansen O, Jensen-Kondering U. Manual thrombus density measurement depends on the method of thrombus delineation. J Stroke. 2018;20(3):411–2.CrossRefGoogle Scholar
  18. 18.
    Angermaier A, Langner S. Thrombus density measurement is promising but technical standards are needed. J Neurointerv Surg. 2017.  https://doi.org/10.1136/neurintsurg-2015-011866.Google Scholar
  19. 19.
    Bourcier R, Pautre R, Mirza M, Castets C, Darcourt J, Labreuche J, et al. MRI quantitative T2* mapping to predict dominant composition of in vitro thrombus. AJNR Am J Neuroradiol. 2019;40(1):59–64.CrossRefGoogle Scholar
  20. 20.
    Bretzner M, Lopes R, McCarthy R, Corseaux D, Auger F, Gunning G, et al. Texture parameters of R2* maps are correlated with iron concentration and red blood cells count in clot analogs: a 7T micro-MRI study. J Neuroradiol. 2019.  https://doi.org/10.1136/j.neurad.2019.10.004.
  21. 21.
    Janot K, Oliveira TR, Fromont-Hankard G, Annan M, Filipiak I, Barantin L, et al. Quantitative estimation of thrombus-erythrocytes using MRI. A phantom study with clot analogs and analysis by statistic regression models. J Neurointerv Surg. 2019.  https://doi.org/10.1136/neurintsurg-2019-014950.
  22. 22.
    Fennell VS, Setlur Nagesh SV, Meess KM, Gutierrez L, James RH, Springer ME, et al. What to do about fibrin rich ‘tough clots’? Comparing the Solitaire stent retriever with a novel geometric clot extractor in an in vitro stroke model. J Neurointerv Surg. 2018;10(9):907–10.CrossRefGoogle Scholar
  23. 23.
    Johnson S, Duffy S, Gunning G, Gilvarry M, McGarry JP, McHugh PE. Review of mechanical testing and modelling of thrombus material for vascular implant and device design. Ann Biomed Eng. 2017;45(11):2494–508.CrossRefGoogle Scholar
  24. 24.
    Duffy S, Farrell M, McArdle K, Thornton J, Vale D, Rainsford E, et al. Novel methodology to replicate clot analogs with diverse composition in acute ischemic stroke. J Neurointerv Surg. 2017;9(5):486–91.CrossRefGoogle Scholar
  25. 25.
    Brinjikji W, Michalak G, Kadirvel R, Dai D, Gilvarry M, Duffy S, et al. Utility of single-energy and dual-energy computed tomography in clot characterization: an in-vitro study. Interv Neuroradiol. 2017;23(3):279–84.CrossRefGoogle Scholar
  26. 26.
    Cecchin E, De Marchi S, Querin F, Marin MG, Fiorentino R, Tesio F. Efficacy of hepatic computed tomography to detect iron overload in chronic hemodialysis. Kidney Int. 1990;37(3):943–50.CrossRefGoogle Scholar
  27. 27.
    Jang S, Graffy PM, Ziemlewicz TJ, Lee SJ, Summers RM, Pickhardt PJ. Opportunistic osteoporosis screening at routine abdominal and thoracic CT: normative L1 trabecular attenuation values in more than 20 000 adults. Radiology. 2019;291(2):360–7.CrossRefGoogle Scholar
  28. 28.
    New PF, Aronow S. Attenuation measurements of whole blood and blood fractions in computed tomography. Radiology. 1976;121(3 Pt. 1):635–40.CrossRefGoogle Scholar
  29. 29.
    Voter WA, Lucaveche C, Erickson HP. Concentration of protein in fibrin fibers and fibrinogen polymers determined by refractive index matching. Biopolymers. 1986;25(12):2375–84.CrossRefGoogle Scholar
  30. 30.
    Brown AE, Litvinov RI, Discher DE, Purohit PK, Weisel JW. Multiscale mechanics of fibrin polymer: gel stretching with protein unfolding and loss of water. Science. 2009;325(5941):741–4.CrossRefGoogle Scholar
  31. 31.
    Gunning GM, McArdle K, Mirza M, Duffy S, Gilvarry M, Brouwer PA. Clot friction variation with fibrin content; implications for resistance to thrombectomy. J Neurointerv Surg. 2018;10(1):34–8.CrossRefGoogle Scholar
  32. 32.
    Weafer FM, Duffy S, Machado I, Gunning G, Mordasini P, Roche E, et al. Characterization of strut indentation during mechanical thrombectomy in acute ischemic stroke clot analogs. J Neurointerv Surg. 2019;11:891–7.CrossRefGoogle Scholar
  33. 33.
    van der Marel K, Chueh JY, Brooks OW, King RM, Marosfoi MG, Langan ET, et al. Quantitative assessment of device-clot interaction for stent retriever thrombectomy. J Neurointerv Surg. 2016;8(12):1278–82.CrossRefGoogle Scholar
  34. 34.
    Chueh JY, Kuhn AL, Puri AS, Wilson SD, Wakhloo AK, Gounis MJ. Reduction in distal emboli with proximal flow control during mechanical thrombectomy: a quantitative in vitro study. Stroke. 2013;44(5):1396–401.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Aglae Velasco Gonzalez
    • 1
    Email author
  • Boris Buerke
    • 1
  • Dennis Görlich
    • 2
  • Manfred Fobker
    • 3
  • Thilo Rusche
    • 1
  • Cristina Sauerland
    • 2
  • Norbert Meier
    • 4
  • Astrid Jeibmann
    • 5
  • Ray McCarthy
    • 6
  • Harald Kugel
    • 4
  • Peter Sporns
    • 1
  • Andreas Faldum
    • 2
  • Werner Paulus
    • 5
  • Walter Heindel
    • 1
  1. 1.Department of Clinical Radiology, NeuroradiologyUniversity Hospital MuensterMuensterGermany
  2. 2.Institute of Biostatistics and Clinical ResearchUniversity of MuensterMuensterGermany
  3. 3.Center for Laboratory MedicineUniversity Hospital MuensterMuensterGermany
  4. 4.Department of Clinical Radiology, Medical PhysicsUniversity Hospital MuensterMuensterGermany
  5. 5.Institute of NeuropathologyUniversity Hospital MuensterMuensterGermany
  6. 6.Cerenovus, Galway Neuro Technology CentreGalwayIreland

Personalised recommendations