Literature DB >> 15817341

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

Arnold C Paulino1, Mary Koshy, Rebecca Howell, David Schuster, Lawrence W Davis.   

Abstract

PURPOSE: To compare the gross tumor volume (GTV) identified on CT to that obtained from fluorodeoxyglucose (FDG) positron emission tomography (PET) and determine the differences in volume and dose coverage of the PET-GTV when the CT-GTV is used for radiotherapy planning. METHODS AND MATERIALS: A total of 40 patients with intact squamous cell carcinoma arising in the head-and-neck region underwent intensity-modulated radiotherapy (IMRT) at one department. All patients underwent CT simulation for treatment planning followed by PET-CT in the treatment position. CT simulation images were fused to the CT component of the PET-CT images. The GTV using the CT simulation images was contoured (CT-GTV), as was the GTV based on the PET scan (PET-GTV). The IMRT plans were obtained using the CT-GTV.
RESULTS: The PET-GTV was smaller, the same size, and larger than the CT-GTV in 30 (75%), 3 (8%), and 7 (18%) cases respectively. The median PET-GTV and CT-GTV volume was 20.3 cm(3) (range, 0.2-294) and 37.2 cm(3) (range, 2-456), respectively. The volume of PET-GTV receiving at least 95% of the prescribed dose was 100% in 20 (50%), 95-99% in 10 (25%), 90-94% in 3 (8%), 85-89% in 1 (3%), 80-84% in 2 (5%), 75-79% in 1 (3%), and <75% in 3 (8%) cases. The minimal dose received by 95% of the PET-GTV was >/=100% in 19 (48%), 95-99% in 11 (28%), 90-94% in 5 (13%), 85-89% in 2 (5%), and <75% in 3 (8%) cases.
CONCLUSION: The PET-GTV was larger than the CT-GTV in 18% of cases. In approximately 25% of patients with intact head-and-neck cancer treated using IMRT, the volume of PET-GTV receiving at least 95% of the prescribed dose and minimal dose received by 95% of the PET-GTV were less than optimal.

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Year:  2005        PMID: 15817341     DOI: 10.1016/j.ijrobp.2004.08.037

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


  62 in total

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Authors:  Joji Kawabe; Shigeaki Higashiyama; Atsushi Yoshida; Kohei Kotani; Susumu Shiomi
Journal:  Jpn J Radiol       Date:  2012-04-05       Impact factor: 2.374

Review 2.  PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques.

Authors:  Habib Zaidi; Issam El Naqa
Journal:  Eur J Nucl Med Mol Imaging       Date:  2010-03-25       Impact factor: 9.236

3.  Prognostic value of metabolic tumor volume and velocity in predicting head-and-neck cancer outcomes.

Authors:  Karen P Chu; James D Murphy; Trang H La; Trevor E Krakow; Andrei Iagaru; Edward E Graves; Annie Hsu; Peter G Maxim; Billy Loo; Daniel T Chang; Quynh-Thu Le
Journal:  Int J Radiat Oncol Biol Phys       Date:  2012-01-21       Impact factor: 7.038

4.  Delineation of FDG-PET tumors from heterogeneous background using spectral clustering.

Authors:  Fei Yang; Perry W Grigsby
Journal:  Eur J Radiol       Date:  2012-01-23       Impact factor: 3.528

5.  From anatomical to biological target volumes: the role of PET in radiation treatment planning.

Authors:  D A X Schinagl; J H A M Kaanders; W J G Oyen
Journal:  Cancer Imaging       Date:  2006-10-31       Impact factor: 3.909

6.  A gradient-based method for segmenting FDG-PET images: methodology and validation.

Authors:  Xavier Geets; John A Lee; Anne Bol; Max Lonneux; Vincent Grégoire
Journal:  Eur J Nucl Med Mol Imaging       Date:  2007-03-13       Impact factor: 9.236

7.  Controversies: is there a role for positron-emission tomographic CT in the initial staging of head and neck squamous cell carcinoma?

Authors:  Suresh K Mukherji; Carol R Bradford
Journal:  AJNR Am J Neuroradiol       Date:  2006-02       Impact factor: 3.825

Review 8.  Balancing risk and reward in target delineation for highly conformal radiotherapy in head and neck cancer.

Authors:  Avraham Eisbruch; Vincent Gregoire
Journal:  Semin Radiat Oncol       Date:  2009-01       Impact factor: 5.934

Review 9.  Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.

Authors:  Tonghe Wang; Yang Lei; Yabo Fu; Walter J Curran; Tian Liu; Jonathon A Nye; Xiaofeng Yang
Journal:  Phys Med       Date:  2020-07-29       Impact factor: 2.685

10.  A segmentation framework towards automatic generation of boost subvolumes for FDG-PET tumors: a digital phantom study.

Authors:  Fei Yang; Perry W Grigsby
Journal:  Eur J Radiol       Date:  2012-07-27       Impact factor: 3.528

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