Literature DB >> 21981879

An evaluation of an automated 4D-CT contour propagation tool to define an internal gross tumour volume for lung cancer radiotherapy.

Stewart Gaede1, Jason Olsthoorn, Alexander V Louie, David Palma, Edward Yu, Brian Yaremko, Belal Ahmad, Jeff Chen, Karl Bzdusek, George Rodrigues.   

Abstract

BACKGROUND AND
PURPOSE: To evaluate an automated 4D-CT contouring propagation tool by its impact on the inter- and intra-physician variability in lung tumour delineation.
MATERIALS AND METHODS: In a previous study, six radiation oncologists contoured the gross tumour volume (GTV) and nodes on 10 phases of the 4D-CT dataset of 10 lung cancer patients to examine the intra- and inter-physician variability. In this study, a model-based deformable image registration algorithm was used to propagate the GTV and nodes on each phase of the same 4D-CT datasets. A blind review of the contours was performed by each physician and edited. Inter- and intra-physician variability for both the manual and automated methods was assessed by calculating the centroid motion of the GTV using the Pearson correlation coefficient and the variability in the internal gross tumour volume (IGTV) overlap using the Dice similarity coefficient (DSC).
RESULTS: The time for manual delineation was (42.7±18.6)min versus (17.7±5.4)min when the propagation tool was used. A significant improvement in the mean Pearson correlation coefficient was also observed. There was a significant decrease in mean DSC in only 1 out of 10 primary IGTVs and 2 out of 10 nodal IGTVs. Intra-physician variability was not significantly impacted (DSC>0.742).
CONCLUSIONS: Automated 4D-CT propagation tools can significantly decrease the IGTV delineation time without significantly decreasing the inter- and intra-physician variability.
Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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Mesh:

Year:  2011        PMID: 21981879     DOI: 10.1016/j.radonc.2011.08.036

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  11 in total

1.  Improving image-guided radiation therapy of lung cancer by reconstructing 4D-CT from a single free-breathing 3D-CT on the treatment day.

Authors:  Guorong Wu; Jun Lian; Dinggang Shen
Journal:  Med Phys       Date:  2012-12       Impact factor: 4.071

2.  Radiotherapy infrastructure and human resources in Switzerland : Present status and projected computations for 2020.

Authors:  Niloy Ranjan Datta; Shaka Khan; Dietmar Marder; Daniel Zwahlen; Stephan Bodis
Journal:  Strahlenther Onkol       Date:  2016-07-25       Impact factor: 3.621

3.  Semiautomated volumetric response evaluation as an imaging biomarker in superior sulcus tumors.

Authors:  C G Vos; M Dahele; J R van Sörnsen de Koste; S Senan; I Bahce; M A Paul; E Thunnissen; E F Smit; K J Hartemink
Journal:  Strahlenther Onkol       Date:  2013-12-22       Impact factor: 3.621

4.  Contour propagation using non-uniform cubic B-splines for lung tumor delineation in 4D-CT.

Authors:  Yongchuan Liu; Renchao Jin; Mi Chen; Enmin Song; Xiangyang Xu; Sheng Zhang; Chih-Cheng Hung
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-07-16       Impact factor: 2.924

5.  Comparison of rigid and deformable image registration for nasopharyngeal carcinoma radiotherapy planning with diagnostic position PET/CT.

Authors:  Yudai Kai; Hidetaka Arimura; Ryo Toya; Tetsuo Saito; Tomohiko Matsuyama; Yoshiyuki Fukugawa; Shinya Shiraishi; Yoshinobu Shimohigashi; Masato Maruyama; Natsuo Oya
Journal:  Jpn J Radiol       Date:  2019-12-13       Impact factor: 2.374

Review 6.  A review of automatic lung tumour segmentation in the era of 4DCT.

Authors:  Nadine Wong Yuzhen; Sarah Barrett
Journal:  Rep Pract Oncol Radiother       Date:  2019-02-22

7.  Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method.

Authors:  Ze Jin; Hidetaka Arimura; Yoshiyuki Shioyama; Katsumasa Nakamura; Jumpei Kuwazuru; Taiki Magome; Hidetake Yabu-Uchi; Hiroshi Honda; Hideki Hirata; Masayuki Sasaki
Journal:  J Radiat Res       Date:  2014-06-30       Impact factor: 2.724

8.  Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy.

Authors:  Shujun Zhang; Bo Lv; Xiangpeng Zheng; Ya Li; Weiqiang Ge; Libo Zhang; Fan Mo; Jianjian Qiu
Journal:  Front Public Health       Date:  2022-03-22

9.  Geometrical differences in gross target volumes between 3DCT and 4DCT imaging in radiotherapy for non-small-cell lung cancer.

Authors:  Fengxing Li; Jianbin Li; Yingjie Zhang; Min Xu; Dongping Shang; Tingyong Fan; Tonghai Liu; Qian Shao
Journal:  J Radiat Res       Date:  2013-04-05       Impact factor: 2.724

10.  Comparison of rigid and deformable registration through the respiratory phases of four-dimensional computed tomography image data sets for radiotherapy after breast-conserving surgery.

Authors:  Aiping Zhang; Jianbin Li; Heng Qiu; Wei Wang; Yanluan Guo
Journal:  Medicine (Baltimore)       Date:  2017-12       Impact factor: 1.817

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