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Improving acute stroke assessment in non-enhanced computed tomography: automated tool for early ischemic lesion volume detection

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Abstract

Background and objectives

ASPECTs is a widely used marker to identify early stroke signs on non-enhanced computed tomography (NECT), yet it presents interindividual variability and it may be hard to use for non-experts. We introduce an algorithm capable of automatically estimating the NECT volumetric extension of early acute ischemic changes in the 3D space. We compared the power of this marker with ASPECTs evaluated by experienced practitioner in predicting the clinical outcome.

Methods

We analyzed and processed neuroimaging data of 153 patients admitted with acute ischemic stroke. All patients underwent a NECT at admission and on follow-up. The developed algorithm identifies the early ischemic hypodense region based on an automatic comparison of the gray level in the images of the two hemispheres, assumed to be an approximate mirror image of each other in healthy patients.

Results

In the two standard axial slices used to estimate the ASPECTs, the regions identified by the algorithm overlap significantly with those identified by experienced practitioners. However, in many patients, the regions identified automatically extend significantly to other slices. In these cases, the volume marker provides supplementary and independent information. Indeed, the clinical outcome of patients with volume marker = 0 can be distinguished with higher statistical confidence than the outcome of patients with ASPECTs = 10.

Conclusion

The volumetric extension and the location of acute ischemic region in the 3D-space, automatically identified by our algorithm, provide data that are mostly in agreement with the ASPECTs value estimated by expert practitioners, and in some cases complementary and independent.

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Acknowledgements

The authors wish to thank Michele Allegra for data processing support.

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Authors and Affiliations

  1. Molecular and Statistical Biophysics Group, International School for Advanced Studies (SISSA), Via Bonomea 265, 34136, Trieste, Italy

    Mara Sabina Bernardi, Alex Rodriguez, Alessandro Laio & Daniele Amati

  2. Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, University Hospital and Health Services of Trieste—ASUGI, University of Trieste, Strada di Fiume 447, 34149, Trieste, Italy

    Paola Caruso, Giovanni Furlanis, Mariana Ridolfi, Gabriele Prandin, Marcello Naccarato & Paolo Manganotti

  3. Dipartimento di Matematica, Informatica e Geoscienze, Università degli studi di Trieste, via Valerio 12/1, 34127, Trieste, Italy

    Alex Rodriguez

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

Corresponding author

Correspondence to Gabriele Prandin.

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We conducted our study according to the principles of the Declaration of Helsinki. All participants released their informed consent to participate in the study after all procedures had been fully explained. Approval for the study had been obtained from the local ethics committee.

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Bernardi, M.S., Rodriguez, A., Caruso, P. et al. Improving acute stroke assessment in non-enhanced computed tomography: automated tool for early ischemic lesion volume detection. Neurol Sci (2024). https://doi.org/10.1007/s10072-024-07339-5

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