Deep learning detection of prostate cancer recurrence with F-FACBC (fluciclovine, Axumin®) positron emission tomography.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Dec 1, 2020

Abstract

PURPOSE: To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal F-FACBC (fluciclovine, Axumin®) PET scans based on the presence of tumor recurrence and/or metastases in patients with prostate cancer (PC) and biochemical recurrence (BCR).

Authors

  • Jong Jin Lee
    Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, 300 Pasteur Dr, Stanford, CA, 94305, USA.
  • Hongye Yang
    DimensionalMechanics Inc.®, Seattle, WA, USA.
  • Benjamin L Franc
    From the Department of Radiology and Biomedical Imaging (Y.D., J.H.S., H.T., R.H., N.W.J., T.P.C., M.S.A., C.M.A., S.C.B., R.R.F., S.Y.H., Y.S., R.A.H., M.H.P., B.L.F.) and Institute for Computational Health Sciences (J.H.S., M.G.K., H.T., D.L., K.A.Z., D.H.), University of California, San Francisco, 550 Parnassus Ave, San Francisco, CA 94143; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, Calif (Y.D.); and Department of Radiology, University of California, Davis, Sacramento, Calif (L.N.).
  • Andrei Iagaru
    Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, 300 Pasteur Dr, Stanford, CA, 94305, USA.
  • Guido A Davidzon
    Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, 300 Pasteur Dr, Stanford, CA, 94305, USA. gdavidzon@stanford.edu.

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