Literature DB >> 27697296

Internal and external validation of an ESTRO delineation guideline - dependent automated segmentation tool for loco-regional radiation therapy of early breast cancer.

Ahmed R Eldesoky1, Esben S Yates2, Tine B Nyeng2, Mette S Thomsen2, Hanne M Nielsen3, Philip Poortmans4, Carine Kirkove5, Mechthild Krause6, Claus Kamby7, Ingvil Mjaaland8, Egil S Blix9, Ingelise Jensen10, Martin Berg11, Ebbe L Lorenzen12, Zahra Taheri-Kadkhoda13, Birgitte V Offersen14.   

Abstract

BACKGROUND AND
PURPOSE: To internally and externally validate an atlas based automated segmentation (ABAS) in loco-regional radiation therapy of breast cancer.
MATERIALS AND METHODS: Structures of 60 patients delineated according to the ESTRO consensus guideline were included in four categorized multi-atlas libraries using MIM Maestro™ software. These libraries were used for auto-segmentation in two different patient groups (50 patients from the local institution and 40 patients from other institutions). Dice Similarity Coefficient, Average Hausdorff Distance, difference in volume and time were computed to compare ABAS before and after correction against a gold standard manual segmentation (MS).
RESULTS: ABAS reduced the time of MS before and after correction by 93% and 32%, respectively. ABAS showed high agreement for lung, heart, breast and humeral head, moderate agreement for chest wall and axillary nodal levels and poor agreement for interpectoral, internal mammary nodal regions and LADCA. Correcting ABAS significantly improved all the results. External validation of ABAS showed comparable results.
CONCLUSIONS: ABAS is a clinically useful tool for segmenting structures in breast cancer loco-regional radiation therapy in a multi-institutional setting. However, manual correction of some structures is important before clinical use. The ABAS is now available for routine clinical use in Danish patients.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Atlas based automated segmentation; Breast cancer; ESTRO consensus guideline; Multi-center study; Target volume delineation; Validation

Mesh:

Year:  2016        PMID: 27697296     DOI: 10.1016/j.radonc.2016.09.005

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


  12 in total

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2.  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

3.  A novel specific grading standard study of auto-segmentation of organs at risk in thorax: subjective-objective-combined grading standard.

Authors:  Yanchen Ying; Hao Wang; Hua Chen; Jianfan Cheng; Hengle Gu; Yan Shao; Yanhua Duan; Aihui Feng; Wen Feng; Xiaolong Fu; Hong Quan; Zhiyong Xu
Journal:  Biomed Eng Online       Date:  2021-06-03       Impact factor: 2.819

4.  Dosimetric assessment of an Atlas based automated segmentation for loco-regional radiation therapy of early breast cancer in the Skagen Trial 1: A multi-institutional study.

Authors:  Ahmed R Eldesoky; Giulio Francolini; Mette S Thomsen; Esben S Yates; Tine B Nyeng; Carine Kirkove; Claus Kamby; Egil S Blix; Mette H Nielsen; Zahra Taheri-Kadkhoda; Martin Berg; Birgitte V Offersen
Journal:  Clin Transl Radiat Oncol       Date:  2017-02-06

5.  Atlas Sampling for Prone Breast Automatic Segmentation of Organs at Risk: The Importance of Patients' Body Mass Index and Breast Cup Size for an Optimized Contouring of the Heart and the Coronary Vessels.

Authors:  Xinzhuo Wang; Raymond Miralbell; Odile Fargier-Bochaton; Shelley Bulling; Jean Paul Vallée; Giovanna Dipasquale
Journal:  Technol Cancer Res Treat       Date:  2020 Jan-Dec

6.  Automated planning of whole breast irradiation using hybrid IMRT improves efficiency and quality.

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Journal:  J Appl Clin Med Phys       Date:  2019-11-19       Impact factor: 2.102

7.  Feasibility of using a novel automatic cardiac segmentation algorithm in the clinical routine of lung cancer patients.

Authors:  Robert Neil Finnegan; Lucia Orlandini; Xiongfei Liao; Jun Yin; Jinyi Lang; Jason Dowling; Davide Fontanarosa
Journal:  PLoS One       Date:  2021-01-14       Impact factor: 3.240

8.  Automatic segmentation of cardiac structures for breast cancer radiotherapy.

Authors:  Jae Won Jung; Choonik Lee; Elizabeth G Mosher; Matthew M Mille; Yeon Soo Yeom; Elizabeth C Jones; Minsoo Choi; Choonsik Lee
Journal:  Phys Imaging Radiat Oncol       Date:  2019-12-05

9.  The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.

Authors:  Danju Huang; Han Bai; Li Wang; Yu Hou; Lan Li; Yaoxiong Xia; Zhirui Yan; Wenrui Chen; Li Chang; Wenhui Li
Journal:  Technol Cancer Res Treat       Date:  2021 Jan-Dec

10.  The Contribution of Thoracic Radiation Dose Volumes to Subsequent Development of Cardiovascular Disease in Cancer Survivors.

Authors:  Carolyn Miller Reilly; Melinda Higgins; Javed Butler; Natia Esiashvili; Baowei Fei; Tommy Flynn; James D Dormer; Eduard Schreibmann
Journal:  J Cardiovasc Nurs       Date:  2022 Sep-Oct 01       Impact factor: 2.468

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