Emerging Applications of Artificial Intelligence in Neuro-Oncology.

Journal: Radiology
Published Date: Jan 22, 2019

Abstract

Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.

Authors

  • 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.).
  • Andreas M Rauschecker
    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.).
  • R Nick Bryan
    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.).
  • 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.
  • 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.).

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