Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Mar 3, 2021

Abstract

BACKGROUND AND PURPOSE: Artificial intelligence advances have stimulated a new generation of autosegmentation, however clinical evaluations of these algorithms are lacking. This study assesses the clinical utility of deep learning-based autosegmentation for MR-based prostate radiotherapy planning.

Authors

  • Elaine Cha
    Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States.
  • Sharif Elguindi
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
  • Ifeanyirochukwu Onochie
    Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States.
  • Daniel Gorovets
    Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States.
  • Joseph O Deasy
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Michael Zelefsky
    Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States.
  • Erin F Gillespie
    Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States. Electronic address: efgillespie@ucsd.edu.

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