Literature DB >> 35124134

Comprehensive Quantitative Evaluation of Variability in Magnetic Resonance-Guided Delineation of Oropharyngeal Gross Tumor Volumes and High-Risk Clinical Target Volumes: An R-IDEAL Stage 0 Prospective Study.

Carlos E Cardenas1, Sanne E Blinde2, Abdallah S R Mohamed3, Sweet Ping Ng4, Cornelis Raaijmakers5, Marielle Philippens5, Alexis Kotte5, Abrahim A Al-Mamgani6, Irene Karam7, David J Thomson8, Jared Robbins9, Kate Newbold10, Clifton D Fuller3, Chris Terhaard5.   

Abstract

PURPOSE: Tumor and target volume manual delineation remains a challenging task in head and neck cancer radiation therapy. The purpose of this study was to conduct a multi-institutional evaluation of manual delineations of gross tumor volume (GTV), high-risk clinical target volume (CTV), parotids, and submandibular glands on treatment simulation magnetic resonance scans of patients with oropharyngeal cancer. METHODS AND MATERIALS: We retrospectively collected pretreatment T1-weighted, T1-weighted with gadolinium contrast, and T2-weighted magnetic resonance imaging scans for 4 patients with oropharyngeal cancer under an institution review board-approved protocol. We provided the scans to 26 radiation oncologists from 7 international cancer centers that participated in this delineation study. We also provide the patients' clinical history and physical examination findings, along with a medical photographic image and radiologic results. We used both the Simultaneous Truth and Performance Level Estimation algorithm and pair-wise comparisons of the contours, using overlap/distance metrics. Lastly, to assess experience and CTV delineation institutional practices, we had participants complete a brief questionnaire.
RESULTS: Large variability was measured between observers' delineations for GTVs and CTVs. The mean Dice similarity coefficient values across all physicians' delineations for GTVp, GTVn, CTVp, and CTVn were 0.77, 0.67, 0.77, and 0.69, respectively, for Simultaneous Truth and Performance Level Estimation algorithm comparison, and 0.67, 0.60, 0.67, and 0.58, respectively, for pair-wise analysis. Normal tissue contours were defined more consistently when considering overlap/distance metrics. The median radiation oncology clinical experience was 7 years. The median experience delineating on magnetic resonance imaging was 3.5 years. The GTV-to-CTV margin used was 10 mm for 6 of 7 participant institutions. One institution used 8 mm, and 3 participants (from 3 different institutions) used a margin of 5 mm.
CONCLUSIONS: The data from this study suggests that appropriate guidelines, contouring quality assurance sessions, and training are still needed for the adoption of magnetic resonance-based treatment planning for head and neck cancers. Such efforts should play a critical role in reducing delineation variation and ensure standardization of target design across clinical practices.
Copyright © 2022. Published by Elsevier Inc.

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Year:  2022        PMID: 35124134      PMCID: PMC9119288          DOI: 10.1016/j.ijrobp.2022.01.050

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   8.013


  44 in total

1.  Impact of magnetic resonance imaging versus CT on nasopharyngeal carcinoma: primary tumor target delineation for radiotherapy.

Authors:  Na-Na Chung; Lai-Lei Ting; Wei-Chung Hsu; Louis Tak Lui; Po-Ming Wang
Journal:  Head Neck       Date:  2004-03       Impact factor: 3.147

2.  Target volume delineation in oropharyngeal cancer: impact of PET, MRI, and physical examination.

Authors:  Anuradha Thiagarajan; Nicola Caria; Heiko Schöder; N Gopalakrishna Iyer; Suzanne Wolden; Richard J Wong; Eric Sherman; Matthew G Fury; Nancy Lee
Journal:  Int J Radiat Oncol Biol Phys       Date:  2011-10-27       Impact factor: 7.038

3.  Interobserver variability in delineation of target volumes in head and neck cancer.

Authors:  Julie van der Veen; Akos Gulyban; Sandra Nuyts
Journal:  Radiother Oncol       Date:  2019-04-29       Impact factor: 6.280

Review 4.  The role of computational methods for automating and improving clinical target volume definition.

Authors:  Jan Unkelbach; Thomas Bortfeld; Carlos E Cardenas; Vincent Gregoire; Wille Hager; Ben Heijmen; Robert Jeraj; Stine S Korreman; Roman Ludwig; Bertrand Pouymayou; Nadya Shusharina; Jonas Söderberg; Iuliana Toma-Dasu; Esther G C Troost; Eliana Vasquez Osorio
Journal:  Radiother Oncol       Date:  2020-10-08       Impact factor: 6.280

5.  Atlas ranking and selection for automatic segmentation of the esophagus from CT scans.

Authors:  Jinzhong Yang; Benjamin Haas; Raymond Fang; Beth M Beadle; Adam S Garden; Zhongxing Liao; Lifei Zhang; Peter Balter; Laurence Court
Journal:  Phys Med Biol       Date:  2017-11-14       Impact factor: 3.609

6.  Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function.

Authors:  Carlos E Cardenas; Rachel E McCarroll; Laurence E Court; Baher A Elgohari; Hesham Elhalawani; Clifton D Fuller; Mona J Kamal; Mohamed A M Meheissen; Abdallah S R Mohamed; Arvind Rao; Bowman Williams; Andrew Wong; Jinzhong Yang; Michalis Aristophanous
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-02-07       Impact factor: 7.038

7.  Improved survival using intensity-modulated radiation therapy in head and neck cancers: a SEER-Medicare analysis.

Authors:  Beth M Beadle; Kai-Ping Liao; Linda S Elting; Thomas A Buchholz; K Kian Ang; Adam S Garden; B Ashleigh Guadagnolo
Journal:  Cancer       Date:  2014-01-13       Impact factor: 6.860

8.  International guideline for the delineation of the clinical target volumes (CTV) for nasopharyngeal carcinoma.

Authors:  Anne W Lee; Wai Tong Ng; Jian Ji Pan; Sharon S Poh; Yong Chan Ahn; Hussain AlHussain; June Corry; Cai Grau; Vincent Grégoire; Kevin J Harrington; Chao Su Hu; Dora L Kwong; Johannes A Langendijk; Quynh Thu Le; Nancy Y Lee; Jin Ching Lin; Tai Xiang Lu; William M Mendenhall; Brian O'Sullivan; Enis Ozyar; Lester J Peters; David I Rosenthal; Yoke Lim Soong; Yungan Tao; Sue S Yom; Joseph T Wee
Journal:  Radiother Oncol       Date:  2017-11-15       Impact factor: 6.280

9.  Uncertainties in target volume delineation in radiotherapy - are they relevant and what can we do about them?

Authors:  Barbara Segedin; Primoz Petric
Journal:  Radiol Oncol       Date:  2016-05-09       Impact factor: 2.991

10.  A prospective in silico analysis of interdisciplinary and interobserver spatial variability in post-operative target delineation of high-risk oral cavity cancers: Does physician specialty matter?

Authors:  Sweet Ping Ng; Brandon A Dyer; Jayashree Kalpathy-Cramer; Abdallah Sherif Radwan Mohamed; Musaddiq J Awan; G Brandon Gunn; Jack Phan; Mark Zafereo; J Matthew Debnam; Carol M Lewis; Rivka R Colen; Michael E Kupferman; Nandita Guha-Thakurta; Guadalupe Canahuate; G Elisabeta Marai; David Vock; Bronwyn Hamilton; John Holland; Carlos E Cardenas; Stephen Lai; David Rosenthal; Clifton David Fuller
Journal:  Clin Transl Radiat Oncol       Date:  2018-08-02
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  2 in total

Review 1.  Target Definition in MR-Guided Adaptive Radiotherapy for Head and Neck Cancer.

Authors:  Mischa de Ridder; Cornelis P J Raaijmakers; Frank A Pameijer; Remco de Bree; Floris C J Reinders; Patricia A H Doornaert; Chris H J Terhaard; Marielle E P Philippens
Journal:  Cancers (Basel)       Date:  2022-06-20       Impact factor: 6.575

2.  Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients.

Authors:  Ruta Zukauskaite; Christopher N Rumley; Christian R Hansen; Michael G Jameson; Yuvnik Trada; Jørgen Johansen; Niels Gyldenkerne; Jesper G Eriksen; Farhannah Aly; Rasmus L Christensen; Mark Lee; Carsten Brink; Lois Holloway
Journal:  Clin Transl Radiat Oncol       Date:  2022-08-06
  2 in total

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