Can CTA-based Machine Learning Identify Patients for Whom Successful Endovascular Stroke Therapy is Insufficient?

Journal: AJNR. American journal of neuroradiology
Published Date: Jun 18, 2025

Abstract

BACKGROUND AND PURPOSE: Despite advances in endovascular stroke therapy (EST) devices and techniques, many patients are left with substantial disability, even if the final infarct volumes (FIVs) remain small. Here, we evaluate the performance of a machine learning (ML) approach using pre-treatment CT angiography (CTA) to identify this cohort of patients that may benefit from additional interventions.

Authors

  • Jerome A Jeevarajan
    From The University of Texas Health Houston, McGovern Medical School, Department of Neurology, Houston, TX, USA (J.A.J., Y.D., A.B., S.S.M., A.N., R.A., S.A.S., L.G.).
  • Yingjun Dong
    McWilliams School of Biomedical Informatics at the University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
  • Anjan Ballekere
    From The University of Texas Health Houston, McGovern Medical School, Department of Neurology, Houston, TX, USA (J.A.J., Y.D., A.B., S.S.M., A.N., R.A., S.A.S., L.G.).
  • Sergio Salazar Marioni
  • Arash Niktabe
    From The University of Texas Health Houston, McGovern Medical School, Department of Neurology, Houston, TX, USA (J.A.J., Y.D., A.B., S.S.M., A.N., R.A., S.A.S., L.G.).
  • Rania Abdelkhaleq
  • Sunil A Sheth
  • Luca Giancardo
    Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.

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