Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial.

Journal: Physics in medicine and biology
Published Date: Sep 28, 2021

Abstract

Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted manually, which is time consuming and resource intensive. Although previous studies mostly focused on automating delineation QA on CT, magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment. In this work, we propose to perform automatic delineation QA on prostate MRI for both the clinical target volume (CTV) and organs-at-risk (OARs) by using delineations generated by 3D Unet variants as benchmarks for QA. These networks were trained on a small gold standard atlas set and applied on a multicentre radiotherapy clinical trial dataset to generate benchmark delineations. Then, a QA stage was designed to recommend 'pass', 'minor correction' and 'major correction' for each manual delineation in the trial set by thresholding its Dice similarity coefficient to the network generated delineation. Among all 3D Unet variants explored, the Unet with anatomical gates in an AtlasNet architecture performed the best in delineation QA, achieving an area under the receiver operating characteristics curve of 0.97, 0.92, 0.89 and 0.97 for identifying unacceptable (major correction) delineations with a sensitivity of 0.93, 0.73, 0.74 and 0.90 at a specificity of 0.93, 0.86, 0.86 and 0.95 for bladder, prostate CTV, rectum and gel spacer respectively. To the best of our knowledge, this is the first study to propose automated delineation QA for a multicentre radiotherapy clinical trial with treatment planning MRI. The methods proposed in this work can potentially improve the accuracy and consistency of CTV and OAR delineation in radiotherapy treatment planning.

Authors

  • Hang Min
    South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia.
  • Jason Dowling
    Australian e-Health Research Centre, CSIRO, Digital Productivity Flagship.
  • Michael G Jameson
    St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Australia.
  • Kirrily Cloak
    Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.
  • Joselle Faustino
    Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia.
  • Mark Sidhom
    South Western Clinical School, University of New South Wales, Australia.
  • Jarad Martin
    Department of Radiation Oncology, Calvary Mater Newcastle, Newcastle, New South Wales, Australia.
  • Martin A Ebert
    School of Physics, University of Western Australia, Western Australia 6009, Australia and Department of Radiation Oncology, Sir Charles Gairdner Hospital, Western Australia 6008, Australia.
  • Annette Haworth
    The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.
  • Phillip Chlap
    South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia.
  • Jeremiah de Leon
    GenesisCare, Sydney, New South Wales, Australia.
  • Megan Berry
    South Western Clinical School, University of New South Wales, Australia.
  • David Pryor
    Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia; School of Medicine, University of Queensland, Australia.
  • Peter Greer
    School of Mathematical and Physical Sciences, University of Newcastle, New South Wales, Australia.
  • Shalini K Vinod
    Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.
  • Lois Holloway
    d Cancer Therapy Centre, Liverpool Hospital , Liverpool , NSW , Australia.

Keywords

External Resources

Popular Topics
Recent Journals