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Ischemic stroke
Original research
Clinical value of automated volumetric quantification of early ischemic tissue changes on non-contrast CT
  1. Gianluca Brugnara1,2,
  2. Peter Mihalicz1,
  3. Christian Herweh1,
  4. Silvia Schönenberger3,
  5. Jan Purrucker4,
  6. Simon Nagel4,5,
  7. Peter Arthur Ringleb4,
  8. Martin Bendszus1,
  9. Markus A Möhlenbruch1,
  10. Ulf Neuberger1,6
  1. 1 Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
  2. 2 Section of Computational Neuroimaging, University Hospital Heidelberg, Heidelberg, Germany
  3. 3 Neurology, Heidelberg University, Heidelberg, Germany
  4. 4 Neurology, University Hospital Heidelberg, Heidelberg, Baden-Württemberg, Germany
  5. 5 Department of Neurology, Städtisches Klinikum Ludwigshafen, Ludwigshafen, Germany
  6. 6 Section of Computational Neuroimaging, Heidelberg University Hospital, Heidelberg, Germany
  1. Correspondence to Dr Ulf Neuberger, Department of Neuroradiology, University Hospital Heidelberg, Heidelberg 69120, Germany; ulf.neuberger{at}med.uni-heidelberg.de

Abstract

Background Quantitative and automated volumetric evaluation of early ischemic changes on non-contrast CT (NCCT) has recently been proposed as a new tool to improve prognostic performance in patients undergoing endovascular therapy (EVT) for acute ischemic stroke (AIS). We aimed to test its clinical value compared with the Alberta Stroke Program Early CT Score (ASPECTS) in a large single-institutional patient cohort.

Methods A total of 1103 patients with AIS due to large vessel occlusion in the M1 or proximal M2 segments who underwent NCCT and EVT between January 2013 and November 2019 were retrospectively enrolled. Acute ischemic volumes (AIV) and ASPECTS were generated from the baseline NCCT through e-ASPECTS (Brainomix). Correlations were tested using Spearman’s coefficient. The predictive capabilities of AIV for a favorable outcome (modified Rankin Scale score at 90 days ≤2) were tested using multivariable logistic regression as well as machine-learning models. Performance of the models was assessed using receiver operating characteristic (ROC) curves and differences were tested using DeLong’s test.

Results Patients with a favorable outcome had a significantly lower AIV (median 12.0 mL (IQR 5.7–21.7) vs 18.8 mL (IQR 9.4–33.9), p<0.001). AIV was highly correlated with ASPECTS (rho=0.78, p<0.001) and weakly correlated with the National Institutes of Health Stroke Scale score at baseline (rho=0.22, p<0.001), and was an independent predictor of an unfavorable clinical outcome (adjusted OR 0.97, 95% CI 0.96 to 0.98). No significant difference was found between machine-learning models using either AIV or ASPECTS or both metrics for predicting a good clinical outcome (p>0.05).

Conclusion AIV is an independent predictor of clinical outcome and presented a non-inferior performance compared with ASPECTS, without clear advantages for prognostic modelling.

  • CT
  • Stroke
  • Thrombectomy

Data availability statement

Data are available upon reasonable request.

https://doi.org/10.1136/jnis-2022-019400

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    Data availability statement

    Data are available upon reasonable request.

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    Footnotes

    • Twitter @GianBrugna

    • Contributors GB, PM, MB, MAM and UN conceived and designed the research. GB, PM, CH, SS, JP, SN, PAR, MB, MAM and UN acquired and analyzed the data. GB, PM and UN performed statistical analysis. GB, PM and UN prepared the first draft of the report. All authors made critical revisions of the manuscript for important intellectual content and approved the final version.

    • Funding GB and UN are sponsored by the Physician-Scientist Program of the Medical Faculty of the University of Heidelberg.

    • Competing interests None declared.

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

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.