Literature DB >> 17153406

Tumor delineation using PET in head and neck cancers: threshold contouring and lesion volumes.

Eric C Ford1, Paul E Kinahan, Lorraine Hanlon, Adam Alessio, Joseph Rajendran, David L Schwartz, Mark Phillips.   

Abstract

Tumor boundary delineation using positron emission tomography (PET) is a promising tool for radiation therapy applications. In this study we quantify the uncertainties in tumor boundary delineation as a function of the reconstruction method, smoothing, and lesion size in head and neck cancer patients using FDG-PET images and evaluate the dosimetric impact on radiotherapy plans. FDG-PET images were acquired for eight patients with a GE Advance PET scanner. In addition, a 20 cm diameter cylindrical phantom with six FDG-filled spheres with volumes of 1.2 to 26.5 cm3 was imaged. PET emission scans were reconstructed with the OSEM and FBP algorithms with different smoothing parameters. PET-based tumor regions were delineated using an automatic contouring function set at progressively higher threshold contour levels and the resulting volumes were calculated. CT-based tumor volumes were also contoured by a physician on coregistered PET/CT patient images. The intensity value of the threshold contour level that returns 100% of the actual volume, I(V100), was measured. We generated intensity-modulated radiotherapy (IMRT) plans for an example head and neck patient, treating 66 Gy to CT-based gross disease and 54 Gy to nodal regions at risk, followed by a boost to the FDG-PET-based tumor. The volumes of PET-based tumors are a sensitive function of threshold contour level for all patients and phantom datasets. A 5% change in threshold contour level can translate into a 200% increase in volume. Phantom data indicate that I(V100) can be set as a fraction, f, of the maximum measured uptake. Fractional threshold values in the cylindrical water phantom range from 0.23 to 0.51. Both the fractional threshold and the threshold-volume curve are dependent on lesion size, with lesions smaller than approximately 5 cm3 displaying a more pronounced sensitivity and larger fractional threshold values. The threshold-volume curves and fractional threshold values also depend on the reconstruction algorithm and smoothing filter with more smoothing requiring a higher fractional threshold contour level. The threshold contour level affects the tumor size, and therefore the ultimate boost dose that is achievable with IMRT. In an example head and neck IMRT plan, the D95 of the planning target volume decreased from 7770 to 7230 cGy for 42% vs. 55% contour threshold levels. PET-based tumor volumes are strongly affected by the choice of threshold level. This can have a significant dosimetric impact. The appropriate threshold level depends on lesion size and image reconstruction parameters. These effects should be carefully considered when using PET contour and/or volume information for radiotherapy applications.

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Year:  2006        PMID: 17153406     DOI: 10.1118/1.2361076

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  28 in total

Review 1.  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

2.  [18F]-Fluorodeoxyglucose Positron Emission Tomography Standardized Uptake Value as a Predictor of Adjuvant Chemotherapy Benefits in Patients With Nasopharyngeal Carcinoma.

Authors:  Te-Chun Hsieh; Ching Yun Hsieh; Tse Yen Yang; Tzu Ting Chen; Chen Yuan Lin; Ching-Chan Lin; Chung Hung Hua; Chang-Fang Chiu; Su-Peng Yeh; Yuh Pyng Sher
Journal:  Oncologist       Date:  2015-04-15

3.  Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma.

Authors:  Habib Zaidi; Mehrsima Abdoli; Carolina Llina Fuentes; Issam M El Naqa
Journal:  Eur J Nucl Med Mol Imaging       Date:  2012-05       Impact factor: 9.236

4.  A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours.

Authors:  Hua Li; Wade L Thorstad; Kenneth J Biehl; Richard Laforest; Yi Su; Kooresh I Shoghi; Eric D Donnelly; Daniel A Low; Wei Lu
Journal:  Med Phys       Date:  2008-08       Impact factor: 4.071

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

Authors:  Carryn M Anderson; Wenqing Sun; John M Buatti; Joan E Maley; Bruno Policeni; Sarah L Mott; John E Bayouth
Journal:  Jacobs J Radiat Oncol       Date:  2014-09

Review 6.  A review on segmentation of positron emission tomography images.

Authors:  Brent Foster; Ulas Bagci; Awais Mansoor; Ziyue Xu; Daniel J Mollura
Journal:  Comput Biol Med       Date:  2014-04-28       Impact factor: 4.589

7.  Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method.

Authors:  Dongfeng Han; John Bayouth; Qi Song; Aakant Taurani; Milan Sonka; John Buatti; Xiaodong Wu
Journal:  Inf Process Med Imaging       Date:  2011

8.  Metabolic tumour volumes measured at staging in lymphoma: methodological evaluation on phantom experiments and patients.

Authors:  Michel Meignan; Myriam Sasanelli; René Olivier Casasnovas; Stefano Luminari; Federica Fioroni; Chiara Coriani; Helene Masset; Emmanuel Itti; Paolo G Gobbi; Francesco Merli; Annibale Versari
Journal:  Eur J Nucl Med Mol Imaging       Date:  2014-02-26       Impact factor: 9.236

Review 9.  18F-FDG PET/CT for image-guided and intensity-modulated radiotherapy.

Authors:  Eric C Ford; Joseph Herman; Ellen Yorke; Richard L Wahl
Journal:  J Nucl Med       Date:  2009-09-16       Impact factor: 10.057

10.  Combining multiple FDG-PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods.

Authors:  Ross J McGurk; James Bowsher; John A Lee; Shiva K Das
Journal:  Med Phys       Date:  2013-04       Impact factor: 4.071

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