Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Charlotte Brouwer*, Djamal Boukerroui, Jorge Oliveira, Padraig Looney, Roel Steenbakkers, J.A. Langendijk, Stefan Both, Mark J Gooding

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Background and purpose: Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring. Materials and methods: A total of 103 clinical head and neck cancer cases, contoured using a commercial deep-learning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019–April 2020). The amount of adjustment performed was calculated, and all cases were registered to a common reference frame for assessment purposes. The median, 10th and 90th percentile of adjustment were calculated and displayed using 3D renderings of structures to visually assess systematic and random adjustment. Results were also compared to inter-observer variation reported previously. Assessment was performed for both the whole structures and for regional sub-structures, and according to the radiation therapy technologist (RTT) who edited the contour. Results: The median amount of adjustment was low for all structures (<2 mm), although large local adjustment was observed for some structures. The median was systematically greater or equal to zero, indicating that the auto-contouring tends to under-segment the desired contour. Conclusion: Auto-contouring performance assessment in routine clinical practice has identified systematic improvements required technically, but also highlighted the need for continued RTT training to ensure adherence to guidelines.

Original languageEnglish
Pages (from-to)54-60
Number of pages7
JournalPhysics and Imaging in Radiation Oncology
Volume16
Early online date2020
DOIs
Publication statusPublished - Oct-2020

Keywords

  • Automatic Segmentation
  • Auto-contouring
  • Deep learning
  • Contour adjustment
  • Head and neck organs at risk
  • Radiotherapy

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