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Original research
Imaging-based prediction of histological clot composition from admission CT imaging
  1. Uta Hanning1,
  2. Peter B Sporns2,
  3. Marios N Psychogios2,
  4. Astrid Jeibmann3,
  5. Jens Minnerup4,
  6. Mathias Gelderblom5,
  7. Karolin Schulte1,
  8. Jawed Nawabi1,6,
  9. Gabriel Broocks1,
  10. Lukas Meyer1,
  11. Hermann Krähling3,
  12. Alex Brehm2,
  13. Moritz Wildgruber7,
  14. Jens Fiehler1,
  15. Helge Kniep1
  1. 1 Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
  2. 2 Department of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland
  3. 3 Institute of Neuropathology, University Hospital Münster, Münster, Germany
  4. 4 Department of Neurology, University Hospital Münster, Münster, Germany
  5. 5 Department of Neurology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
  6. 6 Department of Radiology, Charité School of Medicine and University Hospital Berlin, Berlin, Germany
  7. 7 Department of Radiology, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
  1. Correspondence to Dr Uta Hanning, Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Hamburg, Germany; u.hanning{at}uke.de

Abstract

Background Thrombus composition has been shown to be a major determinant of recanalization success and occurrence of complications in mechanical thrombectomy. The most important parameters of thrombus behavior during interventional procedures are relative fractions of fibrin and red blood cells (RBCs). We hypothesized that quantitative information from admission non-contrast CT (NCCT) and CT angiography (CTA) can be used for machine learning based prediction of thrombus composition.

Methods The analysis included 112 patients with occlusion of the carotid-T or middle cerebral artery who underwent thrombectomy. Thrombi samples were histologically analyzed and fractions of fibrin and RBCs were determined. Thrombi were semi-automatically delineated in CTA scans and NCCT scans were registered to the same space. Two regions of interest (ROIs) were defined for each thrombus: small-diameter ROIs capture vessel walls and thrombi, large-diameter ROIs reflect peri-vascular tissue responses. 4844 quantitative image markers were extracted and evaluated for their ability to predict thrombus composition using random forest algorithms in a nested fivefold cross validation.

Results Test set receiver operating characteristic area under the curve was 0.83 (95% CI 0.80 to 0.87) for differentiating RBC-rich thrombi and 0.84 (95% CI 0.80 to 0.87) for differentiating fibrin-rich thrombi. At maximum Youden-Index, RBC-rich thrombi were identified at 77% sensitivity and 74% specificity; for fibrin-rich thrombi the classifier reached 81% sensitivity at 73% specificity.

Conclusions Machine learning based analysis of admission imaging allows for prediction of clot composition. Perspectively, such an approach could allow selection of clot-specific devices and retrieval procedures for personalized thrombectomy strategies.

  • stroke
  • thrombectomy
  • CT
  • embolic

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Statistics from Altmetric.com

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Footnotes

  • Twitter @Fie0815

  • UH and PBS contributed equally.

  • Correction notice This article has been corrected since it first published. The provenance and peer review statement has been included.

  • Contributors Substantial contributions to conception and design: UH, HK, PBS, JF, MW, MNP. Acquisition and analysis and interpretation of data: AJ, JM, MG, KS, JN, GB, LM, AB, UH. Drafting a significant portion of the manuscript or figures: HK, UH, PBS.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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