Literature DB >> 26025546

Multi-subject atlas-based auto-segmentation reduces interobserver variation and improves dosimetric parameter consistency for organs at risk in nasopharyngeal carcinoma: A multi-institution clinical study.

Chang-Juan Tao1, Jun-Lin Yi2, Nian-Yong Chen3, Wei Ren4, Jason Cheng5, Stewart Tung6, Lin Kong7, Shao-Jun Lin8, Jian-Ji Pan8, Guang-Shun Zhang1, Jiang Hu1, Zhen-Yu Qi1, Jun Ma1, Jia-De Lu4, Di Yan9, Ying Sun10.   

Abstract

BACKGROUND AND
PURPOSE: To assess whether consensus guideline-based atlas-based auto-segmentation (ABAS) reduces interobserver variation and improves dosimetric parameter consistency for organs at risk (OARs) in nasopharyngeal carcinoma (NPC).
MATERIALS AND METHODS: Eight radiation oncologists from 8 institutes contoured 20 OARs on planning CT images of 16 patients via manual contouring and manually-edited ABAS contouring. Interobserver variation [volume coefficient of variation (CV), Dice similarity coefficient (DSC), three-dimensional isocenter difference (3D-ICD)] and dosimetric parameters were compared between the two methods of contouring for each OAR.
RESULTS: Interobserver variation was significant for all OARs in manual contouring, resulting in significant dosimetric parameter variation (P<0.05). Edited ABAS significantly improved multiple metrics and reduced dosimetric parameter variation for most OARs; brainstem, spinal cord, cochleae, temporomandibular joint (TMJ), larynx and pharyngeal constrictor muscle (PCM) obtained most benefit (range of mean DSC, volume CV and main ICD values was 0.36-0.83, 12.1-84.3%, 2.2-5.0mm for manual contouring and 0.42-0.86, 7.2-70.6%, 1.2-3.5mm for edited ABAS contouring, respectively; range of dose CV reduction: 1.0-3.0%).
CONCLUSION: Substantial objective interobserver differences occur during manual contouring, resulting in significant dosimetric parameter variation. Edited ABAS reduced interobserver variation and improved dosimetric parameter consistency, particularly for brainstem, spinal cord, cochleae, TMJ, larynx and PCM.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Atlas-based auto segmentation (ABAS); Contour variation; Dosimetric parameter; Nasopharyngeal carcinoma; Organs at risk

Mesh:

Year:  2015        PMID: 26025546     DOI: 10.1016/j.radonc.2015.05.012

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


  24 in total

1.  Auto-contouring via Automatic Anatomy Recognition of Organs at Risk in Head and Neck Cancer on CT images.

Authors:  Xingyu Wu; Jayaram K Udupa; Yubing Tong; Dewey Odhner; Gargi V Pednekar; Charles B Simone; David McLaughlin; Chavanon Apinorasethkul; John Lukens; Dimitris Mihailidis; Geraldine Shammo; Paul James; Joseph Camaratta; Drew A Torigian
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2018-03-13

2.  Delineation of the larynx as organ at risk in radiotherapy: a contouring course within "Rete Oncologica Piemonte-Valle d'Aosta" network to reduce inter- and intraobserver variability.

Authors:  Domenico Cante; Edoardo Petrucci; Cristina Piva; Valeria Casanova Borca; Piera Sciacero; Maurizio Bertodatto; Caterina Marta; Pierfrancesco Franco; Monica Viale; Giovanni La Valle; Maria Rosa La Porta; Oscar Bertetto
Journal:  Radiol Med       Date:  2016-07-15       Impact factor: 3.469

3.  Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Authors:  Bulat Ibragimov; Lei Xing
Journal:  Med Phys       Date:  2017-02       Impact factor: 4.071

4.  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

5.  AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.

Authors:  Xingyu Wu; Jayaram K Udupa; Yubing Tong; Dewey Odhner; Gargi V Pednekar; Charles B Simone; David McLaughlin; Chavanon Apinorasethkul; Ontida Apinorasethkul; John Lukens; Dimitris Mihailidis; Geraldine Shammo; Paul James; Akhil Tiwari; Lisa Wojtowicz; Joseph Camaratta; Drew A Torigian
Journal:  Med Image Anal       Date:  2019-01-29       Impact factor: 8.545

Review 6.  Nasopharyngeal carcinoma: an evolving paradigm.

Authors:  Kenneth C W Wong; Edwin P Hui; Kwok-Wai Lo; Wai Kei Jacky Lam; David Johnson; Lili Li; Qian Tao; Kwan Chee Allen Chan; Ka-Fai To; Ann D King; Brigette B Y Ma; Anthony T C Chan
Journal:  Nat Rev Clin Oncol       Date:  2021-06-30       Impact factor: 66.675

7.  [Segmentation of organs at risk in nasopharyngeal cancer for radiotherapy using a self-adaptive Unet network].

Authors:  Xin Yang; Xueyan Li; Xiaoting Zhang; Fan Song; Sijuan Huang; Yunfei Xia
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2020-11-30

Review 8.  Targeting the signaling in Epstein-Barr virus-associated diseases: mechanism, regulation, and clinical study.

Authors:  Ya Cao; Longlong Xie; Feng Shi; Min Tang; Yueshuo Li; Jianmin Hu; Lin Zhao; Luqing Zhao; Xinfang Yu; Xiangjian Luo; Weihua Liao; Ann M Bode
Journal:  Signal Transduct Target Ther       Date:  2021-01-12

9.  A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases.

Authors:  Yang Zhong; Yanju Yang; Yingtao Fang; Jiazhou Wang; Weigang Hu
Journal:  Front Oncol       Date:  2021-05-05       Impact factor: 6.244

10.  Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT.

Authors:  Aditi Iyer; Maria Thor; Ifeanyirochukwu Onochie; Jennifer Hesse; Kaveh Zakeri; Eve LoCastro; Jue Jiang; Harini Veeraraghavan; Sharif Elguindi; Nancy Y Lee; Joseph O Deasy; Aditya P Apte
Journal:  Phys Med Biol       Date:  2022-01-17       Impact factor: 3.609

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