Deep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI.

Journal: Acta oncologica (Stockholm, Sweden)
PMID: 34783610

Abstract

BACKGROUND: Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time and increase delineation consistency. In this study, the applicability of deep learning for fully automatic delineation of the gross tumour volume (GTV) in patients with anal squamous cell carcinoma (ASCC) was evaluated for the first time. An extensive comparison of the effects single modality and multimodality combinations of computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) have on automatic delineation quality was conducted.

Authors

  • Aurora Rosvoll Groendahl
    Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
  • Yngve Mardal Moe
    Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
  • Christine Kiran Kaushal
    Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
  • Bao Ngoc Huynh
    Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
  • Espen Rusten
    Department of Medical Physics, Oslo University Hospital, Oslo, Norway.
  • Oliver Tomic
    Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
  • Eivor Hernes
    Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
  • Bettina Hanekamp
    Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
  • Christine Undseth
    Department of Oncology, Oslo University Hospital, Oslo, Norway.
  • Marianne Grønlie Guren
    Department of Oncology, Oslo University Hospital, Oslo, Norway.
  • Eirik Malinen
    b Department of Physics , University of Oslo , Oslo , Norway.
  • Cecilia Marie Futsaether
    Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway. cecilia.futsaether@nmbu.no.

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