Machine Learning-Based Prediction of 6-Month Postoperative Karnofsky Performance Status in Patients with Glioblastoma: Capturing the Real-Life Interaction of Multiple Clinical and Oncologic Factors.

Journal: World neurosurgery
Published Date: Jan 28, 2021

Abstract

OBJECTIVE: Ability to thrive after invasive and intensive treatment is an important parameter to assess in patients with glioblastoma multiforme (GBM). Karnofsky Performance Status (KPS) is used to identify those patients suitable for postoperative radiochemotherapy. The aim of the present study is to investigate whether machine learning (ML)-based models can reliably predict patients' KPS 6 months after surgery.

Authors

  • Giuseppe Maria Della Pepa
    Institute of Neurosurgery, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy.
  • Valerio Maria Caccavella
    Institute of Neurosurgery, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy.
  • Grazia Menna
    Institute of Neurosurgery, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy. Electronic address: mennagrazia@gmail.com.
  • Tamara Ius
    Neurosurgery Unit, Department of Neuroscience, Santa Maria della Misericordia, University Hospital, Udine, Italy.
  • Anna Maria Auricchio
    Institute of Neurosurgery, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy.
  • Silvia Chiesa
    Università Cattolica del Sacro Cuore. silvia.chiesa@ymail.com.
  • Simona Gaudino
    Department of Radiology and Neuroradiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy.
  • Enrico Marchese
    Institute of Neurosurgery, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy.
  • Alessandro Olivi
    Institute of Neurosurgery, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Catholic University, Rome, Italy.

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