Literature DB >> 20378266

Emphasizing conformal avoidance versus target definition for IMRT planning in head-and-neck cancer.

Paul M Harari1, Shiyu Song, Wolfgang A Tomé.   

Abstract

PURPOSE: To describe a method for streamlining the process of elective nodal volume definition for head-and-neck (H&N) intensity-modulated radiotherapy (IMRT) planning. METHODS AND MATERIALS: A total of 20 patients who had undergone curative-intent RT for H&N cancer underwent comprehensive treatment planning using three distinct, plan design techniques: conventional three-field design, target-defined IMRT (TD-IMRT), and conformal avoidance IMRT (CA-IMRT). For each patient, the conventional three-field design was created first, thereby providing the "outermost boundaries" for subsequent IMRT design. In brief, TD-IMRT involved physician contouring of the gross tumor volume, high- and low-risk clinical target volume, and normal tissue avoidance structures on consecutive 1.25-mm computed tomography images. CA-IMRT involved physician contouring of the gross tumor volume and normal tissue avoidance structures only. The overall physician time for each approach was monitored, and the resultant plans were rigorously compared.
RESULTS: The average physician working time for the design of the respective H&N treatment contours was 0.3 hour for the conventional three-field design plan, 2.7 hours for TD-IMRT, and 0.9 hour for CA-IMRT. Dosimetric analysis confirmed that the largest volume of tissue treated to an intermediate (50 Gy) and high (70 Gy) dose occurred with the conventional three-field design followed by CA-IMRT and then TD-IMRT. However, for the two IMRT approaches, comparable results were found in terms of salivary gland and spinal cord protection.
CONCLUSION: CA-IMRT for H&N treatment offers an alternative to TD-IMRT. The overall time for physician contouring was substantially reduced (approximately threefold), yielding a more standardized elective nodal volume. Because of the complexity of H&N IMRT target design, CA-IMRT might ultimately prove a safer and more reliable method to export to general radiation oncology practitioners, particularly those with limited H&N caseload experience. (c) 2010 Elsevier Inc. All rights reserved.

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Mesh:

Year:  2010        PMID: 20378266      PMCID: PMC2905233          DOI: 10.1016/j.ijrobp.2009.09.062

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


  21 in total

Review 1.  Selection and delineation of target volumes in head and neck tumors: beyond ICRU definition.

Authors:  Vincent Grégoire; Jean-François Daisne; Xavier Geets; Peter Levendag
Journal:  Rays       Date:  2003 Jul-Sep

2.  A prospective study of salivary function sparing in patients with head-and-neck cancers receiving intensity-modulated or three-dimensional radiation therapy: initial results.

Authors:  K S Chao; J O Deasy; J Markman; J Haynie; C A Perez; J A Purdy; D A Low
Journal:  Int J Radiat Oncol Biol Phys       Date:  2001-03-15       Impact factor: 7.038

3.  A simplified CT-based definition of the lymph node levels in the node negative neck.

Authors:  O B Wijers; P C Levendag; T Tan; E B van Dieren; J van Sörnsen de Koste; H van der Est; S Senan; P J Nowak
Journal:  Radiother Oncol       Date:  1999-07       Impact factor: 6.280

4.  Xerostomia and its predictors following parotid-sparing irradiation of head-and-neck cancer.

Authors:  A Eisbruch; H M Kim; J E Terrell; L H Marsh; L A Dawson; J A Ship
Journal:  Int J Radiat Oncol Biol Phys       Date:  2001-07-01       Impact factor: 7.038

5.  Determination and delineation of nodal target volumes for head-and-neck cancer based on patterns of failure in patients receiving definitive and postoperative IMRT.

Authors:  K S Clifford Chao; Franz J Wippold; Gokhan Ozyigit; Binh N Tran; James F Dempsey
Journal:  Int J Radiat Oncol Biol Phys       Date:  2002-08-01       Impact factor: 7.038

6.  Patterns of locoregional failure after exclusive IMRT for oropharyngeal carcinoma.

Authors:  Giuseppe Sanguineti; G Brandon Gunn; Eugene J Endres; Gregory Chaljub; Praveena Cheruvu; Brent Parker
Journal:  Int J Radiat Oncol Biol Phys       Date:  2008-05-15       Impact factor: 7.038

7.  Brachytherapy versus surgery in carcinoma of tonsillar fossa and/or soft palate: late adverse sequelae and performance status: can we be more selective and obtain better tissue sparing?

Authors:  Peter Levendag; Wideke Nijdam; Inge Noever; Paul Schmitz; Marjan van de Pol; Dick Sipkema; Cora Braat; Maarten de Boer; Peter Jansen
Journal:  Int J Radiat Oncol Biol Phys       Date:  2004-07-01       Impact factor: 7.038

Review 8.  Intensity-modulated radiation therapy for head and neck cancer: emphasis on the selection and delineation of the targets.

Authors:  Avraham Eisbruch; Robert L Foote; Brian O'Sullivan; Jonathan J Beitler; Bhadrasain Vikram
Journal:  Semin Radiat Oncol       Date:  2002-07       Impact factor: 5.934

9.  Where exactly does failure occur after radiation in head and neck cancer?

Authors:  K Pigott; S Dische; M I Saunders
Journal:  Radiother Oncol       Date:  1995-10       Impact factor: 6.280

10.  Patterns of failure in patients receiving definitive and postoperative IMRT for head-and-neck cancer.

Authors:  K S Clifford Chao; Gokhan Ozyigit; Binh N Tran; Mustafa Cengiz; James F Dempsey; Daniel A Low
Journal:  Int J Radiat Oncol Biol Phys       Date:  2003-02-01       Impact factor: 7.038

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  24 in total

1.  Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours.

Authors:  Karl D Fritscher; Marta Peroni; Paolo Zaffino; Maria Francesca Spadea; Rainer Schubert; Gregory Sharp
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

2.  Segmentation of parotid glands from registered CT and MR images.

Authors:  Domen Močnik; Bulat Ibragimov; Lei Xing; Primož Strojan; Boštjan Likar; Franjo Pernuš; Tomaž Vrtovec
Journal:  Phys Med       Date:  2018-06-19       Impact factor: 2.685

Review 3.  Radiotherapy for head and neck tumours in 2012 and beyond: conformal, tailored, and adaptive?

Authors:  Vincent Grégoire; Robert Jeraj; John Aldo Lee; Brian O'Sullivan
Journal:  Lancet Oncol       Date:  2012-06-28       Impact factor: 41.316

4.  Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network.

Authors:  Zhe Guo; Ning Guo; Kuang Gong; Shun'an Zhong; Quanzheng Li
Journal:  Phys Med Biol       Date:  2019-10-16       Impact factor: 3.609

5.  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

6.  Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer.

Authors:  Chen-Ying Ma; Ju-Ying Zhou; Xiao-Ting Xu; Song-Bing Qin; Miao-Fei Han; Xiao-Huan Cao; Yao-Zong Gao; Lu Xu; Jing-Jie Zhou; Wei Zhang; Le-Cheng Jia
Journal:  BMC Med Imaging       Date:  2022-07-09       Impact factor: 2.795

7.  Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma.

Authors:  Robert Poel; Elias Rüfenacht; Ekin Ermis; Michael Müller; Michael K Fix; Daniel M Aebersold; Peter Manser; Mauricio Reyes
Journal:  Radiat Oncol       Date:  2022-10-22       Impact factor: 4.309

8.  A slice classification model-facilitated 3D encoder-decoder network for segmenting organs at risk in head and neck cancer.

Authors:  Shuming Zhang; Hao Wang; Suqing Tian; Xuyang Zhang; Jiaqi Li; Runhong Lei; Mingze Gao; Chunlei Liu; Li Yang; Xinfang Bi; Linlin Zhu; Senhua Zhu; Ting Xu; Ruijie Yang
Journal:  J Radiat Res       Date:  2021-01-01       Impact factor: 2.724

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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