A Deep Learning-Based Computer Aided Detection (CAD) System for Difficult-to-Detect Brain Metastases.

Journal: International journal of radiation oncology, biology, physics
PMID: 36289038

Abstract

PURPOSE: We sought to develop a computer-aided detection (CAD) system that optimally augments human performance, excelling especially at identifying small inconspicuous brain metastases (BMs), by training a convolutional neural network on a unique magnetic resonance imaging (MRI) data set containing subtle BMs that were not detected prospectively during routine clinical care.

Authors

  • Andrew T Fairchild
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina; Piedmont Radiation Oncology, Winston Salem, North Carolina.
  • Joseph K Salama
    Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina; Radiation Oncology Clinical Service, Durham VA Health Care System, Durham, North Carolina.
  • Walter F Wiggins
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (W.F.W., M.T.C., K.M., S.A.G., E.G., M.H.R., G.C.G., K.P.A.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (W.F.W., M.T.C., K.M., K.P.A.).
  • Bradley G Ackerson
    Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina; Radiation Oncology Clinical Service, Durham VA Health Care System, Durham, North Carolina.
  • Peter E Fecci
    Departments of Neurosurgery, Duke University Medical Center, Durham, North Carolina.
  • John P Kirkpatrick
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, United States of America.
  • Scott R Floyd
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina.
  • Devon J Godfrey
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina. Electronic address: devon.godfrey@duke.edu.

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