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Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients.
Koteva, Vesela; Eiben, Björn; Dunlop, Alex; Gupta, Amit; Gangil, Tarun; Wong, Kee Howe; Breedveld, Sebastiaan; Nill, Simeon; Harrington, Kevin; Oelfke, Uwe.
Afiliação
  • Koteva V; Radiotherapy Physics Modelling, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
  • Eiben B; Radiotherapy Physics Modelling, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
  • Dunlop A; The Joint Department of Physics, The Royal Marsden Hospital and The Institute of Cancer Research, London, United Kingdom.
  • Gupta A; Head and Neck Unit, The Royal Marsden National Health Service (NHS) Foundation Trust and The Institute of Cancer Research, London, United Kingdom.
  • Gangil T; Radiotherapy Physics Modelling, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
  • Wong KH; Head and Neck Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom.
  • Breedveld S; Department of Radiotherapy, Erasmus University Medical Center (MC) Rotterdam, Rotterdam, Netherlands.
  • Nill S; Radiotherapy Physics Modelling, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
  • Harrington K; The Joint Department of Physics, The Royal Marsden Hospital and The Institute of Cancer Research, London, United Kingdom.
  • Oelfke U; Targeted Radiotherapy, Department of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
Front Oncol ; 14: 1358350, 2024.
Article em En | MEDLINE | ID: mdl-38549943
ABSTRACT

Background:

MR-Linac allows for daily online treatment adaptation to the observed geometry of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer (HNC) patients takes 45-75 minutes, making it unsuitable for online adaptive radiotherapy. This study aims to clinically and dosimetrically validate an in-house developed algorithm which automatically delineates the elective target volume and OARs for HNC patients in under a minute.

Methods:

Auto-contours were generated by an in-house model with 2D U-Net architecture trained and tested on 52 MRI scans via leave-one-out cross-validation. A randomized selection of 684 automated and manual contours (split half-and-half) was presented to an oncologist to perform a blind test and determine the clinical acceptability. The dosimetric impact was investigated for 13 patients evaluating the differences in dosage for all structures.

Results:

Automated contours were generated in 8 seconds per MRI scan. The blind test concluded that 114 (33%) of auto-contours required adjustments with 85 only minor and 15 (4.4%) of manual contours required adjustments with 12 only minor. Dosimetric analysis showed negligible dosimetric differences between clinically acceptable structures and structures requiring minor changes. The Dice Similarity coefficients for the auto-contours ranged from 0.66 ± 0.11 to 0.88 ± 0.06 across all structures.

Conclusion:

Majority of auto-contours were clinically acceptable and could be used without any adjustments. Majority of structures requiring minor adjustments did not lead to significant dosimetric differences, hence manual adjustments were needed only for structures requiring major changes, which takes no longer than 10 minutes per patient.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Reino Unido