Deep Learning Approaches for Glioblastoma Prognosis in Resource-Limited Settings: A Study Using Basic Patient Demographic, Clinical, and Surgical Inputs.

Journal: World neurosurgery
PMID: 37088416

Abstract

BACKGROUND: Glioblastoma (GBM) is the most common brain tumor in the United States, with an annual incidence rate of 3.21 per 100,000. It is the most aggressive type of diffuse glioma and has a median survival of months after treatment. This study aims to assess the accuracy of different novel deep learning models trained on a set of simple clinical, demographic, and surgical variables to assist in clinical practice, even in areas with constrained health care infrastructure.

Authors

  • Marc Ghanem
    Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon.
  • Abdul Karim Ghaith
    Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA.
  • Cameron Zamanian
    Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA.
  • Antonio Bon-Nieves
    Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA.
  • Archis Bhandarkar
    Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA.
  • Mohamad Bydon
    4Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.
  • Alfredo Quiñones-Hinojosa
    Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.

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