Literature DB >> 20958937

A review of methods of analysis in contouring studies for radiation oncology.

Michael G Jameson1, Lois C Holloway, Philip J Vial, Shalini K Vinod, Peter E Metcalfe.   

Abstract

Inter-observer variability in anatomical contouring is the biggest contributor to uncertainty in radiation treatment planning. Contouring studies are frequently performed to investigate the differences between multiple contours on common datasets. There is, however, no widely accepted method for contour comparisons. The purpose of this study is to review the literature on contouring studies in the context of radiation oncology, with particular consideration of the contouring comparison methods they employ. A literature search, not limited by date, was conducted using Medline and Google Scholar with key words: contour, variation, delineation, inter/intra observer, uncertainty and trial dummy-run. This review includes a description of the contouring processes and contour comparison metrics used. The use of different processes and metrics according to tumour site and other factors were also investigated with limitations described. A total of 69 relevant studies were identified. The most common tumour sites were prostate (26), lung (10), head and neck cancers (8) and breast (7).The most common metric of comparison was volume used 59 times, followed by dimension and shape used 36 times, and centre of volume used 19 times. Of all 69 publications, 67 used a combination of metrics and two used only one metric for comparison. No clear relationships between tumour site or any other factors that may influence the contouring process and the metrics used to compare contours were observed from the literature. Further studies are needed to assess the advantages and disadvantages of each metric in various situations.
© 2010 The Authors. Journal of Medical Imaging and Radiation Oncology © 2010 The Royal Australian and New Zealand College of Radiologists.

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Mesh:

Year:  2010        PMID: 20958937     DOI: 10.1111/j.1754-9485.2010.02192.x

Source DB:  PubMed          Journal:  J Med Imaging Radiat Oncol        ISSN: 1754-9477            Impact factor:   1.735


  34 in total

1.  The utility of atlas-assisted segmentation in the male pelvis is dependent on the interobserver agreement of the structures segmented.

Authors:  K A Langmack; C Perry; C Sinstead; J Mills; D Saunders
Journal:  Br J Radiol       Date:  2014-08-29       Impact factor: 3.039

2.  Reliability and accuracy assessment of Radiation Therapy Oncology Group-endorsed guidelines for brachial plexus contouring.

Authors:  Joris Van de Velde; Tom Vercauteren; Werner De Gersem; Johan Wouters; Katrien Vandecasteele; Philippe Vuye; Frank Vanpachtenbeke; Katharina D'Herde; Ingrid Kerckaert; Wilfried De Neve; Tom Van Hoof
Journal:  Strahlenther Onkol       Date:  2014-04-09       Impact factor: 3.621

3.  Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning.

Authors:  Cristina Suárez-Mejías; Jose Antonio Pérez-Carrasco; Carmen Serrano; Jose Luis López-Guerra; Carlos Parra-Calderón; Tomás Gómez-Cía; Begoña Acha
Journal:  Med Biol Eng Comput       Date:  2016-04-21       Impact factor: 2.602

4.  Postmastectomy radiotherapy for left-sided breast cancer patients: Comparison of advanced techniques.

Authors:  Yibo Xie; Daniel Bourgeois; Beibei Guo; Rui Zhang
Journal:  Med Dosim       Date:  2019-05-23       Impact factor: 1.482

Review 5.  Radiomics: the process and the challenges.

Authors:  Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies
Journal:  Magn Reson Imaging       Date:  2012-08-13       Impact factor: 2.546

6.  Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network.

Authors:  Fangjie Liu; Wanqi Chen; Zhikai Liu; Yinjie Tao; Xia Liu; Fuquan Zhang; Jing Shen; Hui Guan; Hongnan Zhen; Shaobin Wang; Qi Chen; Yu Chen; Xiaorong Hou
Journal:  Cancer Manag Res       Date:  2021-11-02       Impact factor: 3.989

7.  Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

Authors:  Maria Thor; Aditya Apte; Rabia Haq; Aditi Iyer; Eve LoCastro; Joseph O Deasy
Journal:  Int J Radiat Oncol Biol Phys       Date:  2020-11-13       Impact factor: 7.038

8.  3D Variation in delineation of head and neck organs at risk.

Authors:  Charlotte L Brouwer; Roel J H M Steenbakkers; Edwin van den Heuvel; Joop C Duppen; Arash Navran; Henk P Bijl; Olga Chouvalova; Fred R Burlage; Harm Meertens; Johannes A Langendijk; Aart A van 't Veld
Journal:  Radiat Oncol       Date:  2012-03-13       Impact factor: 3.481

Review 9.  Machine learning applications in radiation oncology.

Authors:  Matthew Field; Nicholas Hardcastle; Michael Jameson; Noel Aherne; Lois Holloway
Journal:  Phys Imaging Radiat Oncol       Date:  2021-06-24

10.  Distance deviation measure of contouring variability.

Authors:  Peter Rogelj; Robert Hudej; Primoz Petric
Journal:  Radiol Oncol       Date:  2013-02-01       Impact factor: 2.991

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