Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT.

Journal: Medical physics
Published Date: Oct 1, 2018

Abstract

BACKGROUND: Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The existing dose-volume histogram-based metric may grossly under/overestimate the therapeutic toxicity after 27% in liver RT and 50% in head-and-neck RT. We propose the novel paradigm for toxicity prediction by leveraging the enormous potential of deep learning and go beyond the existing dose/volume histograms.

Authors

  • Bulat Ibragimov
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, 94305, USA.
  • Diego Toesca
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
  • Daniel Chang
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
  • Yixuan Yuan
    Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong.
  • Albert Koong
    Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, California 94305.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.

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