First Report On Physician Assessment and Clinical Acceptability of Custom-Retrained Artificial Intelligence Models for Clinical Target Volume and Organs-at-Risk Auto-Delineation for Postprostatectomy Patients.

Abstract

Authors

• Dean Hobbis
  Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
• Nathan Y Yu
  Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ (Y.H., J.H., S.H.P., N.Y.Y., W.L.); Cornell University, Ithaca, NY (Y.H.); Department of Electric Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA (Y.H.); and Department of Radiation Oncology, Mayo Clinic, Rochester, MN (A.B., E.L.M., D.K.E., D.M.R., S.S., C.L.H., B.E.B., M.W.).
• Karl W Mund
  Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
• Jingwei Duan
  Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
• Jean-Claude M Rwigema
  Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
• William W Wong
  Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
• Steven E Schild
  Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
• Sameer R Keole
  Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
• Xue Feng
  Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia.
• Quan Chen
  Management School, Zhongshan Institute, University of Electronic Science and Technology of China, Guangdong, 528402, China.
• Carlos E Vargas
  Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
• Yi Rong
  Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.

Keywords

External Resources