Collateral Automation for Triage in Stroke: Evaluating Automated Scoring of Collaterals in Acute Stroke on Computed Tomography Scans.

Journal: Cerebrovascular diseases (Basel, Switzerland)
PMID: 31216543

Abstract

Computed tomography angiography (CTA) collateral scoring can identify patients most likely to benefit from mechanical thrombectomy and those more likely to have good outcomes and ranges from 0 (no collaterals) to 3 (complete collaterals). In this study, we used a machine learning approach to categorise the degree of collateral flow in 98 patients who were eligible for mechanical thrombectomy and generate an e-CTA collateral score (CTA-CS) for each patient (e-STROKE SUITE, Brainomix Ltd., Oxford, UK). Three experienced neuroradiologists (NRs) independently estimated the CTA-CS, first without and then with knowledge of the e-CTA output, before finally agreeing on a consensus score. Addition of the e-CTA improved the intraclass correlation coefficient (ICC) between NRs from 0.58 (0.46-0.67) to 0.77 (0.66-0.85, p = 0.003). Automated e-CTA, without NR input, agreed with the consensus score in 90% of scans with the remaining 10% within 1 point of the consensus (ICC 0.93, 0.90-0.95). Sensitivity and specificity for identifying favourable collateral flow (collateral score 2-3) were 0.99 (0.93-1.00) and 0.94 (0.70-1.00), respectively. e-CTA correlated with the Alberta Stroke Programme Early CT Score (Spearman correlation 0.46, p < 0.001) highlighting the value of good collateral flow in maintaining tissue viability prior to reperfusion. In conclusion, -e-CTA provides a real-time and fully automated approach to collateral scoring with the potential to improve consistency of image interpretation and to independently quantify collateral scores even without expert rater input.

Authors

  • Iris Q Grunwald
    Neuroscience, Anglia Ruskin University, School of Medicine, Chelmsford, United Kingdom, iqgrunwald@gmail.com.
  • Johann Kulikovski
    Department of Neuroradiology, Saarland University Hospital, Homburg, Germany.
  • Wolfgang Reith
    Department of Neuroradiology, Saarland University Hospital, Homburg, Germany.
  • Stephen Gerry
    Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom.
  • Rafael Namias
    Brainomix Limited, Oxford, United Kingdom.
  • Maria Politi
    Department for Neuroradiology, Bremen Hospital, Bremen, Germany.
  • Panagiotis Papanagiotou
    Department for Neuroradiology, Bremen Hospital, Bremen, Germany.
  • Marco Essig
    Department of Radiology, University of Manitoba, Winnipeg, Manitoba, Canada.
  • Shrey Mathur
    Department of Neurology, Saarland University Hospital, Homburg-Saar, Germany.
  • Olivier Joly
    Brainomix Limited, Oxford, United Kingdom.
  • Khawar Hussain
    Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom.
  • Viola Wagner
    Department of Neurology, Saarland University Hospital, Homburg-Saar, Germany.
  • Sweni Shah
    Neuroscience, Anglia Ruskin University, School of Medicine, Chelmsford, United Kingdom.
  • George Harston
    Stroke Medicine, Oxford University Hospitals NHS Trust, Oxford, United Kingdom.
  • Julija Vlahovic
    Neuroscience, Anglia Ruskin University, School of Medicine, Chelmsford, United Kingdom.
  • Silke Walter
    Department of Neurology, Saarland University Hospital, Homburg-Saar, Germany.
  • Anna Podlasek
    NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, Nottingham, United Kingdom.
  • Klaus Fassbender
    Clinic and Polyclinic for Neurology, Saarland University Homburg, Germany.

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