Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Oct 22, 2020

Abstract

PURPOSE: This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model, via transfer learning with minimal input data, to three different internal treatment planning styles and one external institution planning style.

Authors

  • Roya Norouzi Kandalan
    Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, USA; Department of Electrical Engineering, University of North Texas, Denton, USA.
  • Dan Nguyen
    University of Massachusetts Chan Medical School, Worcester, Massachusetts.
  • Nima Hassan Rezaeian
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
  • Ana M Barragán-Montero
    Molecular Imaging, Radiotherapy and Oncology (MIRO), UCLouvain, Brussels, Belgium.
  • Sebastiaan Breedveld
    Department of Radiation Oncology, Erasmus University Medical Center - Cancer Institute, Rotterdam, The Netherlands.
  • Kamesh Namuduri
    Department of Electrical Engineering, University of North Texas, Denton, USA.
  • Steve Jiang
  • Mu-Han Lin
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.

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