Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.

Journal: Journal of neuro-oncology
Published Date:

Abstract

PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care.

Authors

  • Patrick H Luckett
    Washington University in St. Louis, St. Louis, Missouri, USA.
  • Michael O Olufawo
    Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
  • Ki Yun Park
    Department of Neurological Surgery, Washington University School of Medicine, St Louis, Missouri, USA.
  • Bidhan Lamichhane
    Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
  • Donna Dierker
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
  • Gabriel Trevino Verastegui
    Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
  • John J Lee
    Echocardiography Laboratory Mount Sinai Heart InstituteMount Sinai Medical Center Miami Beach FL.
  • Peter Yang
    Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA.
  • Albert Kim
    From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women's Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.).
  • Omar H Butt
    Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
  • Milan G Chheda
    Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
  • Abraham Z Snyder
    Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA. Electronic address: avi@npg.wustl.edu.
  • Joshua S Shimony
    Department of Pediatrics, Washington University School of Medicine, Saint Louis, Missouri; Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri.
  • Eric C Leuthardt
    Department of Neurological Surgery, Washington University School of Medicine, St Louis, Missouri, USA.