Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma.

Journal: Cancer
Published Date: Mar 4, 2020

Abstract

BACKGROUND: Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represent the effects of treatment, or pseudo-progression (PsP). We hypothesize that quantitative machine learning analysis of clinically acquired multiparametric magnetic resonance imaging (mpMRI) can identify subvisual imaging characteristics to provide robust, noninvasive imaging signatures that can distinguish true progression (TP) from PsP.

Authors

  • Hamed Akbari
    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.
  • Saima Rathore
    DCIS, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan; DCS&IT, University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir. Electronic address: saimarathore_2k6@yahoo.com.
  • Spyridon Bakas
    Perelman School of Medicine, Philadelphia, PA, USA.
  • MacLean P Nasrallah
    Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Gaurav Shukla
    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Elizabeth Mamourian
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Martin Rozycki
    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.
  • Stephen J Bagley
    Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Jeffrey D Rudie
    Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., L.X., A.K., J.M.E., T.C., I.M.N., S.M., J.C.G.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (J.D.R., A.M.R.); Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pa (X.L., J.W.); University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (M.T.D.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); Department of Radiology, University of Texas, Austin, Tex (R.N.B.); and Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pa (I.M.N.).
  • Adam E Flanders
  • Adam P Dicker
    Shivank Garg, Noelle L. Williams, and Adam P. Dicker, Sidney Kimmel Medical College at Thomas Jefferson University Philadelphia, PA; and Andrew Ip, Winship Cancer Institute at Emory University, Atlanta, GA.
  • Arati S Desai
    Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Donald M O'Rourke
    Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Steven Brem
    Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Robert Lustig
    Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Suyash Mohan
    Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., L.X., A.K., J.M.E., T.C., I.M.N., S.M., J.C.G.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (J.D.R., A.M.R.); Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pa (X.L., J.W.); University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (M.T.D.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); Department of Radiology, University of Texas, Austin, Tex (R.N.B.); and Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pa (I.M.N.).
  • Ronald L Wolf
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Michel Bilello
    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Maria Martinez-Lage
    Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Christos Davatzikos
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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