Literature DB >> 25568889

Interobserver and intermodality variability in GTV delineation on simulation CT, FDG-PET, and MR Images of Head and Neck Cancer.

Carryn M Anderson1, Wenqing Sun2, John M Buatti3, Joan E Maley4, Bruno Policeni, Sarah L Mott, John E Bayouth.   

Abstract

PURPOSE: To compare the interobserver and intermodality differences in image-based identification of head and neck primary site gross tumor volumes (GTV). Modalities compared include: contrast-enhanced CT, F-18 fluorodeoxyglucose positron emission tomography (PET/CT) and contrast-enhanced MRI. METHODS AND MATERIALS: Fourteen patients were simulated after immobilization for all 3 imaging modalities (CT, PET/CT, MRI). Three radiation oncologists (RO) contoured GTVs as seen on each modality. The GTV was contoured first on the contrast-enhanced CT (considered the standard), then on PET/CT, and finally on post-contrast T1 MRI. Interobserver and intermodality variability were analyzed by volume, intersection, union, and volume overlap ratio (VOR).
RESULTS: Analysis of RO contours revealed the average volume for CT-, PET/CT-, and MRI-derived GTVs were 45cc, 35cc and 49cc, respectively. In 93% of cases PET/CT-derived GTVs had the smallest volume and in 57% of cases MRI-derived GTVs had the largest volume. CT showed the largest variation in target definition (standard deviation amongst observers 35%) compared to PET/CT (28%) and MRI (27%). The VOR was largest (indicating greatest interobserver agreement) in PET/CT (46%), followed by MRI (36%), followed by CT (34%). For each observer, the least agreement in GTV definition occurred between MRI & PET/CT (average VOR = 41%), compared to CT & PET/CT (48%) and CT & MRI (47%).
CONCLUSIONS: A nonsignificant interobserver difference in GTVs for each modality was seen. Among three modalities, CT was least consistent, while PET/CT-derived GTVs had the smallest volumes and were most consistent. MRI combined with PET/CT provided the least agreement in GTVs generated. The significance of these differences for head & neck cancer is important to explore as we move to volume-based treatment planning based on multi-modality imaging as a standard method for treatment delivery.

Entities:  

Year:  2014        PMID: 25568889      PMCID: PMC4283948     

Source DB:  PubMed          Journal:  Jacobs J Radiat Oncol


  24 in total

Review 1.  The impact of gross tumor volume (GTV) and clinical target volume (CTV) definition on the total accuracy in radiotherapy theoretical aspects and practical experiences.

Authors:  Elisabeth Weiss; Clemens F Hess
Journal:  Strahlenther Onkol       Date:  2003-01       Impact factor: 3.621

2.  Comparison of CT- and FDG-PET-defined gross tumor volume in intensity-modulated radiotherapy for head-and-neck cancer.

Authors:  Arnold C Paulino; Mary Koshy; Rebecca Howell; David Schuster; Lawrence W Davis
Journal:  Int J Radiat Oncol Biol Phys       Date:  2005-04-01       Impact factor: 7.038

3.  Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers.

Authors:  Stephen L Breen; Julia Publicover; Shiroma De Silva; Greg Pond; Kristy Brock; Brian O'Sullivan; Bernard Cummings; Laura Dawson; Anne Keller; John Kim; Jolie Ringash; Eugene Yu; Aaron Hendler; John Waldron
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-03-26       Impact factor: 7.038

4.  The potential impact of CT-MRI matching on tumor volume delineation in advanced head and neck cancer.

Authors:  C Rasch; R Keus; F A Pameijer; W Koops; V de Ru; S Muller; A Touw; H Bartelink; M van Herk; J V Lebesque
Journal:  Int J Radiat Oncol Biol Phys       Date:  1997-11-01       Impact factor: 7.038

5.  Initial experience of FDG-PET/CT guided IMRT of head-and-neck carcinoma.

Authors:  Dian Wang; Christopher J Schultz; Paul A Jursinic; Mirek Bialkowski; X Ronald Zhu; W Douglas Brown; Scott D Rand; Michelle A Michel; Bruce H Campbell; Stuart Wong; X Allen Li; J Frank Wilson
Journal:  Int J Radiat Oncol Biol Phys       Date:  2006-05-01       Impact factor: 7.038

6.  Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen.

Authors:  Jean-François Daisne; Thierry Duprez; Birgit Weynand; Max Lonneux; Marc Hamoir; Hervé Reychler; Vincent Grégoire
Journal:  Radiology       Date:  2004-08-18       Impact factor: 11.105

7.  A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning.

Authors:  Sara Leibfarth; David Mönnich; Stefan Welz; Christine Siegel; Nina Schwenzer; Holger Schmidt; Daniel Zips; Daniela Thorwarth
Journal:  Acta Oncol       Date:  2013-07-23       Impact factor: 4.089

8.  Tumor volumes measured from static and dynamic 18F-fluoro-2-deoxy-D-glucose positron emission tomography-computed tomography scan: comparison of different methods using magnetic resonance imaging as the criterion standard.

Authors:  Hanwei Chen; Jinzhao Jiang; Junling Gao; Dan Liu; Jan Axelsson; Minyi Cui; Nan-Jie Gong; Shi-Ting Feng; Liangping Luo; Bingsheng Huang
Journal:  J Comput Assist Tomogr       Date:  2014 Mar-Apr       Impact factor: 1.826

9.  Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning.

Authors:  Huan Yu; Curtis Caldwell; Katherine Mah; Daniel Mozeg
Journal:  IEEE Trans Med Imaging       Date:  2009-03       Impact factor: 10.048

10.  The effect of dental artifacts, contrast media, and experience on interobserver contouring variations in head and neck anatomy.

Authors:  Jennifer C O'Daniel; David I Rosenthal; Adam S Garden; Jerry L Barker; Anesa Ahamad; K Kian Ang; Joshua A Asper; Angel I Blanco; Renaud de Crevoisier; F Christopher Holsinger; Chirag B Patel; David L Schwartz; He Wang; Lei Dong
Journal:  Am J Clin Oncol       Date:  2007-04       Impact factor: 2.339

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  15 in total

1.  Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma.

Authors:  Pengfei Yang; Lei Xu; Zuozhen Cao; Yidong Wan; Yi Xue; Yangkang Jiang; Eric Yen; Chen Luo; Jing Wang; Yi Rong; Tianye Niu
Journal:  Mol Imaging Biol       Date:  2020-12       Impact factor: 3.488

2.  Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211.

Authors:  Mathieu Hatt; John A Lee; Charles R Schmidtlein; Issam El Naqa; Curtis Caldwell; Elisabetta De Bernardi; Wei Lu; Shiva Das; Xavier Geets; Vincent Gregoire; Robert Jeraj; Michael P MacManus; Osama R Mawlawi; Ursula Nestle; Andrei B Pugachev; Heiko Schöder; Tony Shepherd; Emiliano Spezi; Dimitris Visvikis; Habib Zaidi; Assen S Kirov
Journal:  Med Phys       Date:  2017-05-18       Impact factor: 4.071

3.  CT-based volumetric tumor growth velocity: A novel imaging prognostic indicator in oropharyngeal cancer patients receiving radiotherapy.

Authors:  Subha Perni; Abdallah S R Mohamed; Jacob Scott; Heiko Enderling; Adam S Garden; G Brandon Gunn; David I Rosenthal; Clifton D Fuller
Journal:  Oral Oncol       Date:  2016-11-04       Impact factor: 5.337

4.  Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas.

Authors:  Thibault Marin; Yue Zhuo; Rita Maria Lahoud; Fei Tian; Xiaoyue Ma; Fangxu Xing; Maryam Moteabbed; Xiaofeng Liu; Kira Grogg; Nadya Shusharina; Jonghye Woo; Ruth Lim; Chao Ma; Yen-Lin E Chen; Georges El Fakhri
Journal:  Radiother Oncol       Date:  2021-11-19       Impact factor: 6.280

5.  Recurrent oropharyngeal cancer after organ preserving treatment: pattern of failure and survival.

Authors:  M de Ridder; Z A R Gouw; J J Sonke; A Navran; B Jasperse; J Heukelom; M E T Tesselaar; W M C Klop; M W M van den Brekel; Abrahim Al-Mamgani
Journal:  Eur Arch Otorhinolaryngol       Date:  2016-12-09       Impact factor: 2.503

Review 6.  Application of positron emission tomography/computed tomography in radiation treatment planning for head and neck cancers.

Authors:  Musaddiq J Awan; Farzan Siddiqui; David Schwartz; Jiankui Yuan; Mitchell Machtay; Min Yao
Journal:  World J Radiol       Date:  2015-11-28

Review 7.  Imaging for Target Delineation and Treatment Planning in Radiation Oncology: Current and Emerging Techniques.

Authors:  Sonja Stieb; Brigid McDonald; Mary Gronberg; Grete May Engeseth; Renjie He; Clifton David Fuller
Journal:  Hematol Oncol Clin North Am       Date:  2019-09-17       Impact factor: 3.722

8.  Characterization of positioning uncertainties in PET-CT-MR trimodality solutions for radiotherapy.

Authors:  Pauline Hinault; Isabelle Gardin; Pierrick Gouel; Pierre Decazes; Sebastien Thureau; Ovidiu Veresezan; Henri Souchay; Pierre Vera; David Gensanne
Journal:  J Appl Clin Med Phys       Date:  2022-04-28       Impact factor: 2.243

9.  Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images.

Authors:  Mohamed A Naser; Lisanne V van Dijk; Renjie He; Kareem A Wahid; Clifton D Fuller
Journal:  Head Neck Tumor Segm (2020)       Date:  2021-01-13

10.  Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning.

Authors:  Roque Rodríguez Outeiral; Paula Bos; Abrahim Al-Mamgani; Bas Jasperse; Rita Simões; Uulke A van der Heide
Journal:  Phys Imaging Radiat Oncol       Date:  2021-07-02
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