Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.

Journal: Medical physics
Published Date: Feb 21, 2022

Abstract

BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process requires accurate and precise segmentation of the prostate and organs at risk (OARs), which is laborious and time-consuming when contoured manually. Artificial intelligence (AI)-based auto-segmentation has the potential to significantly accelerate the radiation therapy treatment planning process; however, the accuracy of auto-segmentation needs to be validated before its full clinical adoption.

Authors

  • Jingwei Duan
    Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
  • Mark Bernard
    Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
  • Laura Downes
    Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
  • Brooke Willows
    Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
  • Xue Feng
    Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia.
  • Waleed F Mourad
    Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
  • William St Clair
    Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
  • Quan Chen
    Management School, Zhongshan Institute, University of Electronic Science and Technology of China, Guangdong, 528402, China.

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