Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 33264639

Abstract

OBJECTIVE: Dose prediction using deep learning networks prior to radiotherapy might lead tomore efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess their clinical usage for paediatric abdominal tumours.

Authors

  • F Guerreiro
    Department of Radiotherapy, University Medical Center Utrecht, The Netherlands. Electronic address: F.Guerreiro@umcutrecht.nl.
  • E Seravalli
    Department of Radiotherapy, University Medical Center Utrecht, The Netherlands. Electronic address: E.Seravalli@umcutrecht.nl.
  • G O Janssens
    Department of Radiation Oncology, University Medical Center Utrecht, The Netherlands; Princess Máxima Center for Pediatric Oncology, The Netherlands. Electronic address: G.O.R.Janssens@umcutrecht.nl.
  • J H Maduro
    Princess Máxima Center for Pediatric Oncology, The Netherlands; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Electronic address: j.h.maduro@umcg.nl.
  • A C Knopf
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Electronic address: a.c.knopf@umcg.nl.
  • J A Langendijk
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Electronic address: j.a.langendijk@umcg.nl.
  • B W Raaymakers
    Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.
  • C Kontaxis
    Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands.

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