Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT-methylation status, age and sex.

Journal: Acta neurochirurgica
PMID: 39992425

Abstract

PURPOSE: Machine Learning (ML) has become an essential tool for analyzing biomedical data, facilitating the prediction of treatment outcomes and patient survival. However, the effectiveness of ML models heavily relies on both the choice of algorithms and the quality of the input data. In this study, we aimed to develop a novel predictive model to estimate individual survival for patients diagnosed with glioblastoma (GBM), focusing on key variables such as O6-Methylguanine-DNA Methyltransferase (MGMT) methylation status, age, and sex.

Authors

  • Emanuele Maragno
    Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1 A, 48149, Münster, Germany.
  • Sarah Ricchizzi
    Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1 A, 48149, Münster, Germany.
  • Nils Ralf Winter
    Universitätsklinikum Münster Klinik für Psychiatrie und Psychotherapie.
  • Sönke Josua Hellwig
    Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1 A, 48149, Münster, Germany.
  • Walter Stummer
    Department of Neurosurgery, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany.
  • Tim Hahn
  • Markus Holling
    Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1 A, 48149, Münster, Germany. hollingm@ukmuenster.de.

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