Literature DB >> 28816167

Variabilities of Magnetic Resonance Imaging-, Computed Tomography-, and Positron Emission Tomography-Computed Tomography-Based Tumor and Lymph Node Delineations for Lung Cancer Radiation Therapy Planning.

Kishor Karki1, Siddharth Saraiya2, Geoffrey D Hugo1, Nitai Mukhopadhyay3, Nuzhat Jan1, Jessica Schuster1, Matthew Schutzer1, Lester Fahrner4, Robert Groves4, Kathryn M Olsen5, John C Ford6, Elisabeth Weiss7.   

Abstract

PURPOSE: To investigate interobserver delineation variability for gross tumor volumes of primary lung tumors and associated pathologic lymph nodes using magnetic resonance imaging (MRI), and to compare the results with computed tomography (CT) alone- and positron emission tomography (PET)-CT-based delineations. METHODS AND MATERIALS: Seven physicians delineated the tumor volumes of 10 patients for the following scenarios: (1) CT only, (2) PET-CT fusion images registered to CT ("clinical standard"), and (3) postcontrast T1-weighted MRI registered with diffusion-weighted MRI. To compute interobserver variability, the median surface was generated from all observers' contours and used as the reference surface. A physician labeled the interface types (tumor to lung, atelectasis (collapsed lung), hilum, mediastinum, or chest wall) on the median surface. Contoured volumes and bidirectional local distances between individual observers' contours and the reference contour were analyzed.
RESULTS: Computed tomography- and MRI-based tumor volumes normalized relative to PET-CT-based volumes were 1.62 ± 0.76 (mean ± standard deviation) and 1.38 ± 0.44, respectively. Volume differences between the imaging modalities were not significant. Between observers, the mean normalized volumes per patient averaged over all patients varied significantly by a factor of 1.6 (MRI) and 2.0 (CT and PET-CT) (P=4.10 × 10-5 to 3.82 × 10-9). The tumor-atelectasis interface had a significantly higher variability than other interfaces for all modalities combined (P=.0006). The interfaces with the smallest uncertainties were tumor-lung (on CT) and tumor-mediastinum (on PET-CT and MRI).
CONCLUSIONS: Although MRI-based contouring showed overall larger variability than PET-CT, contouring variability depended on the interface type and was not significantly different between modalities, despite the limited observer experience with MRI. Multimodality imaging and combining different imaging characteristics might be the best approach to define the tumor volume most accurately.
Copyright © 2017 Elsevier Inc. All rights reserved.

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Year:  2017        PMID: 28816167      PMCID: PMC5607632          DOI: 10.1016/j.ijrobp.2017.05.002

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  38 in total

Review 1.  Geometrical analysis of radiotherapy target volume delineation: a systematic review of reported comparison methods.

Authors:  G G Hanna; A R Hounsell; J M O'Sullivan
Journal:  Clin Oncol (R Coll Radiol)       Date:  2010-09       Impact factor: 4.126

Review 2.  Contribution of MRI in lung cancer staging.

Authors:  A Khalil; T Bouhela; M-F Carette
Journal:  JBR-BTR       Date:  2013 May-Jun

3.  A novel method for comparing 3D target volume delineations in radiotherapy.

Authors:  R W van der Put; B W Raaymakers; E M Kerkhof; M van Vulpen; J J W Lagendijk
Journal:  Phys Med Biol       Date:  2008-04-01       Impact factor: 3.609

4.  Target delineation variability and corresponding margins of peripheral early stage NSCLC treated with stereotactic body radiotherapy.

Authors:  Heike Peulen; José Belderbos; Matthias Guckenberger; Andrew Hope; Inga Grills; Marcel van Herk; Jan-Jakob Sonke
Journal:  Radiother Oncol       Date:  2015-03-11       Impact factor: 6.280

5.  Superior sulcus tumors: CT and MR imaging.

Authors:  R T Heelan; B E Demas; J F Caravelli; N Martini; M S Bains; P M McCormack; M Burt; D M Panicek; A Mitzner
Journal:  Radiology       Date:  1989-03       Impact factor: 11.105

Review 6.  Volumetric uncertainty in radiotherapy.

Authors:  C S Hamilton; M A Ebert
Journal:  Clin Oncol (R Coll Radiol)       Date:  2005-09       Impact factor: 4.126

7.  Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis.

Authors:  Roel J H M Steenbakkers; Joop C Duppen; Isabelle Fitton; Kirsten E I Deurloo; Lambert J Zijp; Emile F I Comans; Apollonia L J Uitterhoeve; Patrick T R Rodrigus; Gijsbert W P Kramer; Johan Bussink; Katrien De Jaeger; José S A Belderbos; Peter J C M Nowak; Marcel van Herk; Coen R N Rasch
Journal:  Int J Radiat Oncol Biol Phys       Date:  2005-09-28       Impact factor: 7.038

8.  Combined correction of recovery effect and motion blur for SUV quantification of solitary pulmonary nodules in FDG PET/CT.

Authors:  Ivayla Apostolova; Rafael Wiemker; Timo Paulus; Sven Kabus; Thomas Dreilich; Jörg van den Hoff; Michail Plotkin; Janos Mester; Winfried Brenner; Ralph Buchert; Susanne Klutmann
Journal:  Eur Radiol       Date:  2010-03-20       Impact factor: 5.315

9.  CT and (18)F-deoxyglucose (FDG) image fusion for optimization of conformal radiotherapy of lung cancers.

Authors:  P Giraud; D Grahek; F Montravers; M F Carette; E Deniaud-Alexandre; F Julia; J C Rosenwald; J M Cosset; J N Talbot; M Housset; E Touboul
Journal:  Int J Radiat Oncol Biol Phys       Date:  2001-04-01       Impact factor: 7.038

10.  Impact of anatomical location on value of CT-PET co-registration for delineation of lung tumors.

Authors:  Isabelle Fitton; Roel J H M Steenbakkers; Kenneth Gilhuijs; Joop C Duppen; Peter J C M Nowak; Marcel van Herk; Coen R N Rasch
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-11-05       Impact factor: 7.038

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  9 in total

1.  Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT.

Authors:  Yu Zhao; Andrei Gafita; Bernd Vollnberg; Giles Tetteh; Fabian Haupt; Ali Afshar-Oromieh; Bjoern Menze; Matthias Eiber; Axel Rominger; Kuangyu Shi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12-07       Impact factor: 9.236

Review 2.  Magnetic resonance imaging in precision radiation therapy for lung cancer.

Authors:  Hannah Bainbridge; Ahmed Salem; Rob H N Tijssen; Michael Dubec; Andreas Wetscherek; Corinne Van Es; Jose Belderbos; Corinne Faivre-Finn; Fiona McDonald
Journal:  Transl Lung Cancer Res       Date:  2017-12

3.  Unpaired Cross-Modality Educed Distillation (CMEDL) for Medical Image Segmentation.

Authors:  Jue Jiang; Andreas Rimner; Joseph O Deasy; Harini Veeraraghavan
Journal:  IEEE Trans Med Imaging       Date:  2022-05-02       Impact factor: 11.037

4.  Reduction of inter-observer variability using MRI and CT fusion in delineating of primary tumor for radiotherapy in lung cancer with atelectasis.

Authors:  Hongjiao Zhang; Chengrui Fu; Min Fan; Liyong Lu; Yiru Chen; Chengxin Liu; Hongfu Sun; Qian Zhao; Dan Han; Baosheng Li; Wei Huang
Journal:  Front Oncol       Date:  2022-08-03       Impact factor: 5.738

5.  The impact of a radiologist-led workshop on MRI target volume delineation for radiotherapy.

Authors:  Shivani Kumar; Lois Holloway; Dale Roach; Elise Pogson; Jacqueline Veera; Vikneswary Batumalai; Karen Lim; Geoff P Delaney; Elizabeth Lazarus; Nira Borok; Daniel Moses; Michael G Jameson; Shalini Vinod
Journal:  J Med Radiat Sci       Date:  2018-08-03

6.  Variability of Gross Tumor Volume Delineation for Stereotactic Body Radiotherapy of the Lung With Tri-60Co Magnetic Resonance Image-Guided Radiotherapy System (ViewRay): A Comparative Study With Magnetic Resonance- and Computed Tomography-Based Target Delineation.

Authors:  Chan Woo Wee; Hyun Joon An; Hyun-Cheol Kang; Hak Jae Kim; Hong-Gyun Wu
Journal:  Technol Cancer Res Treat       Date:  2018-01-01

7.  Comparison of gross tumor volumes of pulmonary metastasis defined by CT and MRI in 0.345 T MRI-guided radiotherapy.

Authors:  Yukihiro Hama; Etsuko Tate
Journal:  BJR Open       Date:  2020-07-31

8.  Comparison of gross target volumes based on four-dimensional CT, positron emission tomography-computed tomography, and magnetic resonance imaging in thoracic esophageal cancer.

Authors:  Huimin Li; Fengxiang Li; Jianbin Li; Youzhe Zhu; Yingjie Zhang; Yanluan Guo; Min Xu; Qian Shao; Xijun Liu
Journal:  Cancer Med       Date:  2020-06-08       Impact factor: 4.452

9.  Does magnetic resonance imaging improve soft tissue sarcoma contouring for radiotherapy?

Authors:  Alexander John Vickers; Niluja Thiruthaneeswaran; Catherine Coyle; Prakash Manoharan; James Wylie; Lucy Kershaw; Ananya Choudhury; Alan Mcwilliam
Journal:  BJR Open       Date:  2019-05-13
  9 in total

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