Literature DB >> 22815411

Interobserver variation in parotid gland delineation: a study of its impact on intensity-modulated radiotherapy solutions with a systematic review of the literature.

S W Loo1, W M C Martin, P Smith, S Cherian, T W Roques.   

Abstract

OBJECTIVES: This study evaluates the interobserver variation in parotid gland delineation and its impact on intensity-modulated radiotherapy (IMRT) solutions.
METHODS: The CT volumetric data sets of 10 patients with oropharyngeal squamous cell carcinoma who had been treated with parotid-sparing IMRT were used. Four radiation oncologists and three radiologists delineated the parotid gland that had been spared using IMRT. The dose-volume histogram (DVH) for each study contour was calculated using the IMRT plan actually delivered for that patient. This was compared with the original DVH obtained when the plan was used clinically.
RESULTS: 70 study contours were analysed. The mean parotid dose achieved during the actual treatment was within 10% of 24 Gy for all cases. Using the study contours, the mean parotid dose obtained was within 10% of 24 Gy for only 53% of volumes by radiation oncologists and 55% of volumes by radiologists. The parotid DVHs of 46% of the study contours were sufficiently different from those used clinically, such that a different IMRT plan would have been produced.
CONCLUSION: Interobserver variation in parotid gland delineation is significant. Further studies are required to determine ways of improving the interobserver consistency in parotid gland definition.

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Mesh:

Year:  2012        PMID: 22815411      PMCID: PMC3587103          DOI: 10.1259/bjr/32038456

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  71 in total

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2.  Oral health-related quality of life in southern Chinese following radiotherapy for nasopharyngeal carcinoma.

Authors:  A S McMillan; E H N Pow; W K Leung; M C M Wong; D L W Kwong
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Authors:  Jay S Cooper; Suresh K Mukherji; Alicia Y Toledano; Clifford Beldon; Ilona M Schmalfuss; Robert Amdur; Scott Sailer; Laurie A Loevner; Phil Kousouboris; K Kian Ang; Jean Cormack; JoRean Sicks
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-01-08       Impact factor: 7.038

4.  Changes in salivary gland function after radiotherapy of head and neck tumors measured by quantitative pertechnetate scintigraphy: comparison of intensity-modulated radiotherapy and conventional radiation therapy with and without Amifostine.

Authors:  Marc W Münter; Simone Hoffner; Holger Hof; Klaus K Herfarth; Uwe Haberkorn; Volker Rudat; Peter Huber; Jürgen Debus; Christian P Karger
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Authors:  Valerie K Reed; Wendy A Woodward; Lifei Zhang; Eric A Strom; George H Perkins; Welela Tereffe; Julia L Oh; T Kuan Yu; Isabelle Bedrosian; Gary J Whitman; Thomas A Buchholz; Lei Dong
Journal:  Int J Radiat Oncol Biol Phys       Date:  2008-09-17       Impact factor: 7.038

6.  Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers.

Authors:  Stephen L Breen; Julia Publicover; Shiroma De Silva; Greg Pond; Kristy Brock; Brian O'Sullivan; Bernard Cummings; Laura Dawson; Anne Keller; John Kim; Jolie Ringash; Eugene Yu; Aaron Hendler; John Waldron
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-03-26       Impact factor: 7.038

Review 7.  Late effects of radiation therapy in the head and neck region.

Authors:  J S Cooper; K Fu; J Marks; S Silverman
Journal:  Int J Radiat Oncol Biol Phys       Date:  1995-03-30       Impact factor: 7.038

8.  Differences in target outline delineation from CT scans of brain tumours using different methods and different observers.

Authors:  M Yamamoto; Y Nagata; K Okajima; T Ishigaki; R Murata; T Mizowaki; M Kokubo; M Hiraoka
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Authors:  Coen Rasch; Avraham Eisbruch; Peter Remeijer; Luc Bos; Mischa Hoogeman; Marcel van Herk; Joos V Lebesque
Journal:  Int J Radiat Oncol Biol Phys       Date:  2002-01-01       Impact factor: 7.038

10.  Parotid gland sparing IMRT for head and neck cancer improves xerostomia related quality of life.

Authors:  C M van Rij; W D Oughlane-Heemsbergen; A H Ackerstaff; E A Lamers; A J M Balm; C R N Rasch
Journal:  Radiat Oncol       Date:  2008-12-09       Impact factor: 3.481

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  11 in total

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Review 2.  Vision 20/20: perspectives on automated image segmentation for radiotherapy.

Authors:  Gregory Sharp; Karl D Fritscher; Vladimir Pekar; Marta Peroni; Nadya Shusharina; Harini Veeraraghavan; Jinzhong Yang
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3.  Inter-observer variability of clinical target volume delineation in definitive radiotherapy of neck lymph node metastases from unknown primary. A cooperative study of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) Head and Neck Group.

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Journal:  Radiol Med       Date:  2019-03-09       Impact factor: 3.469

4.  Quantifying the dosimetric impact of organ-at-risk delineation variability in head and neck radiation therapy in the context of patient setup uncertainty.

Authors:  Eric Aliotta; Hamidreza Nourzadeh; Jeffrey Siebers
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5.  Parotid glands dose-effect relationships based on their actually delivered doses: implications for adaptive replanning in radiation therapy of head-and-neck cancer.

Authors:  Klaudia U Hunter; Laura L Fernandes; Karen A Vineberg; Daniel McShan; Alan E Antonuk; Craig Cornwall; Mary Feng; Mathew J Schipper; James M Balter; Avraham Eisbruch
Journal:  Int J Radiat Oncol Biol Phys       Date:  2013-09-10       Impact factor: 7.038

Review 6.  [Target volume concepts in radiotherapy and their implications for imaging].

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Journal:  Radiologe       Date:  2018-08       Impact factor: 0.635

7.  Convolutional neural network-based automatic liver delineation on contrast-enhanced and non-contrast-enhanced CT images for radiotherapy planning.

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Journal:  Rep Pract Oncol Radiother       Date:  2020-10-02

8.  A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy.

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Journal:  Technol Cancer Res Treat       Date:  2017-02-07

Review 9.  Challenges for Quality Assurance of Target Volume Delineation in Clinical Trials.

Authors:  Amy Tien Yee Chang; Li Tee Tan; Simon Duke; Wai-Tong Ng
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10.  Automated Quality Assurance of OAR Contouring for Lung Cancer Based on Segmentation With Deep Active Learning.

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Journal:  Front Oncol       Date:  2020-07-03       Impact factor: 6.244

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