A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients.

Journal: Journal of neuro-oncology
Published Date: Jul 19, 2022

Abstract

INTRODUCTION: Glioblastomas (GBMs) are highly aggressive tumors. A common clinical challenge after standard of care treatment is differentiating tumor progression from treatment-related changes, also known as pseudoprogression (PsP). Usually, PsP resolves or stabilizes without further treatment or a course of steroids, whereas true progression (TP) requires more aggressive management. Differentiating PsP from TP will affect the patient's outcome. This study investigated using deep learning to distinguish PsP MRI features from progressive disease.

Authors

  • Mana Moassefi
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • Shahriar Faghani
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • Gian Marco Conte
    Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
  • Roman O Kowalchuk
    Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA.
  • Sanaz Vahdati
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • David J Crompton
    Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL, USA.
  • Carlos Perez-Vega
    Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.
  • Ricardo A Domingo Cabreja
    Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.
  • Sujay A Vora
    Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA.
  • Alfredo Quiñones-Hinojosa
    Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.
  • Ian F Parney
    Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA.
  • Daniel M Trifiletti
    Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL, USA.
  • Bradley J Erickson
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.

Keywords

External Resources

Popular Topics
Recent Journals