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Workshop on Clinical Image-Based Procedures

International Workshop on Multimodal Learning for Clinical Decision Support

CLIP 2020, ML-CDS 2020: Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures pp 24–33Cite as

A Radiomics-Based Machine Learning Approach to Assess Collateral Circulation in Ischemic Stroke on Non-contrast Computed Tomography

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12445))

Abstract

Assessment of collateral circulation in ischemic stroke, which can identify patients for the most appropriate treatment strategies, is currently conducted with visual inspection by a radiologist. Yet numerous studies have shown that visual inspection suffers from inter and intra-rater variability. We present an automatic evaluation of collaterals using radiomic features and machine learning based on the ASPECTS scoring terminology with non-contrast computed tomography (NCCT). The method includes ASPECTS regions identification, extraction of radiomic features, and classification of collateral scores with support vector machines (SVM). Experiments are performed on a dataset of 64 ischemic stroke patients to classify collateral circulation as good, intermediate, or poor and yield an overall area under the curve (AUC) of 0.86 with an average sensitivity of 80.33% and specificity of 79.33%. Thus, we show the feasibility of using automatic evaluation of collateral circulation using NCCT when compared to the ASPECTS score by radiologists using 4D CT angiography as ground truth.

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Acknowledgements

This study was funded by NSERC Discovery Grant RGPIN-2020-04612, NSERC-N01759, ENCS FRS-VE0236 and Fonds de recherche du Quebec – Nature et technologies (FRQNT Grant F01296). The author Y. Xiao is supported by BrainsCAN and CIHR fellowships.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, 1455 boul. De Maisonneuve O., Montreal, QC, H3G 1M8, Canada

    Mumu Aktar, Yiming Xiao & Marta Kersten-Oertel

  2. Department of Diagnostic Radiology, Kingston Health Sciences Centre, Kingston General Hospital, Kingston, ON, K7L 2V7, Canada

    Donatella Tampieri

  3. Department of Electrical and Computer Engineering, Concordia University, 1455 boul. De Maisonneuve O., Montreal, QC, H3G 1M8, Canada

    Hassan Rivaz

Authors
  1. Mumu Aktar
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  2. Yiming Xiao
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  3. Donatella Tampieri
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  4. Hassan Rivaz
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  5. Marta Kersten-Oertel
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Corresponding author

Correspondence to Mumu Aktar .

Editor information

Editors and Affiliations

  1. IBM Almaden Research Center, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  2. Aachen University of Applied Sciences, Aachen, Germany

    Klaus Drechsler

  3. Tel Aviv University, Ramat Aviv, Israel

    Hayit Greenspan

  4. Case Western Reserve University, Cleveland, OH, USA

    Anant Madabhushi

  5. IBM Almaden Research Center, San Jose, CA, USA

    Alexandros Karargyris

  6. Children’s National Hospital, Washington, DC, USA

    Marius George Linguraru

  7. Fraunhofer-Institute for Computer Graphics Research (IGD), Darmstadt, Germany

    Cristina Oyarzun Laura

  8. Children’s National Hospital, Washington, DC, USA

    Raj Shekhar

  9. Fraunhofer-Institute for Computer Graphics Research (IGD), Darmstadt, Germany

    Stefan Wesarg

  10. Universitat Pompeu Fabra, Barcelona, Spain

    Miguel Ángel González Ballester

  11. Fraunhofer Singapore, Singapore, Singapore

    Marius Erdt

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Aktar, M., Xiao, Y., Tampieri, D., Rivaz, H., Kersten-Oertel, M. (2020). A Radiomics-Based Machine Learning Approach to Assess Collateral Circulation in Ischemic Stroke on Non-contrast Computed Tomography. In: Syeda-Mahmood, T., et al. Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures. CLIP ML-CDS 2020 2020. Lecture Notes in Computer Science(), vol 12445. Springer, Cham. https://doi.org/10.1007/978-3-030-60946-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-60946-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60945-0

  • Online ISBN: 978-3-030-60946-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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