Predicting intraoperative 5-ALA-induced tumor fluorescence via MRI and deep learning in gliomas with radiographic lower-grade characteristics.

Journal: Journal of neuro-oncology
PMID: 39560696

Abstract

PURPOSE: Lower-grade gliomas typically exhibit 5-aminolevulinic acid (5-ALA)-induced fluorescence in only 20-30% of cases, a rate that can be increased by doubling the administered dose of 5-ALA. Fluorescence can depict anaplastic foci, which can be precisely sampled to avoid undergrading. We aimed to analyze whether a deep learning model could predict intraoperative fluorescence based on preoperative magnetic resonance imaging (MRI).

Authors

  • Eric Suero Molina
    Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
  • Ghasem Azemi
    Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.
  • Zeynep Ozdemir
  • Carlo Russo
    Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.
  • Hermann Krähling
    Clinic for Radiology, University Hospital Münster, Münster, Germany.
  • Alexandra Valls Chavarria
    Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1, A1, 48149, Münster, Germany.
  • Sidong Liu
    Save Sight Institute, Sydney Medical School, The University of Sydney, Sydney, Australia; Brain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia.
  • Walter Stummer
    Department of Neurosurgery, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany.
  • Antonio Di Ieva
    Neurosurgery Unit, Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.

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