Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

Journal: GigaScience
PMID: 39185700

Abstract

BACKGROUND: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome.

Authors

  • Thomas Roetzer-Pejrimovsky
    Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria.
  • Karl-Heinz Nenning
    Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA.
  • Barbara Kiesel
    Department of Neurosurgery, Medical University of Vienna, Vienna, Austria; and.
  • Johanna Klughammer
    Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80539 Munich, Germany.
  • Martin Rajchl
  • Bernhard Baumann
    Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria.
  • Georg Langs
    Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab, Medical University of Vienna Vienna Austria.
  • Adelheid Woehrer
    Institute of Neurology, Medical University of Vienna, Vienna, Austria.

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