Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy.

Journal: Medical physics
Published Date: Jun 3, 2020

Abstract

PURPOSE: Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation-induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during dose delivery. The existing RT planning solutions generally discard spatial dose distribution information and lose the ability to recognize radiosensitive regions of healthy organs potentially linked to toxicity manifestation. This study proposes a universal deep learning-based algorithm for recognitions of consistent dose patterns and generation of toxicity risk maps for the abdominal area.

Authors

  • Bulat Ibragimov
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, 94305, USA.
  • Diego A S Toesca
  • Daniel T Chang
    Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, California 94305.
  • Yixuan Yuan
    Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong.
  • Albert C Koong
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Ivan R Vogelius
    Department of Oncology, Faulty of Health & Medical Sciences, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.

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