Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Published Date: Nov 4, 2023

Abstract

PURPOSE: There is increasing interest in novel prognostic tools and predictive biomarkers to help identify, with more certainty, cerebral cavernous malformations (CCM) susceptible of bleeding if left untreated. We developed explainable quantitative-based machine learning models from magnetic resonance imaging (MRI) in a large CCM cohort to demonstrate the value of artificial intelligence and radiomics in complementing natural history studies for hemorrhage and functional outcome prediction.

Authors

  • Mohamed Sobhi Jabal
    Department of Radiology, Mayo Clinic, Rochester, MN, United States. Electronic address: jabal.mohamedsobhi@mayo.edu.
  • Marwa A Mohammed
    Department of Radiology, Mayo Clinic, Rochester, MN, United States.
  • Hassan Kobeissi
    Department of Radiology, Mayo Clinic, Rochester, MN, United States.
  • Giuseppe Lanzino
    Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA.
  • Waleed Brinjikji
    Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America.
  • Kelly D Flemming
    Department of Neurology, Mayo Clinic, Rochester, MN, United States.

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