Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-19T03:52:45.619Z Has data issue: false hasContentIssue false

The reliability of quantitative thresholding methods for PET aided delineation of GTVs in Head and Neck tumours

Published online by Cambridge University Press:  24 May 2012

S. Barrett*
Affiliation:
Radiation Therapy Department, Galway Clinic, Doughiska, Galway, Ireland
R. Appleyard
Affiliation:
Sheffield Hallam University, Sheffield, UK
*
Correspondence to: Sarah Barrett, Radiotherapy Department, Galway Clinic, Doughishka, Galway, Ireland. Tel: 00353 86 1712277/00353 91 785646, Fax: 00353 91 785573. Email: [email protected]

Abstract

Introduction: PET–CT scans are commonly used for the purpose of gross tumour volume (GTV) delineation in head and neck cancers. Qualitative visual methods (QVM) are currently employed in most radiotherapy departments but these are subject to inter- and intra-observer variability. Quantitative thresholding methods which appear in the published literature are evaluated with respect to their reliability for delineation of GTVs in head and neck cancers.

Discussion: Image segmentation involves the application of a distinct value to all pixels or voxels in an image dataset. This is a complex process affected by numerous variables. Some of the following segmentation thresholds may be applied to automatically delineate specified regions. Standardised uptake value (SUV) is commonly used to apply a threshold for GTV delineation, however this leads to inappropriately large GTVs. A further common quantitative threshold is based on the maximum signal on the PET image relative to the background uptake, known as signal to background ratio (SBR). This method generates GTVs that correlate well with surgically removed tumour volumes. Applying a fixed threshold of a percentage of the maximal intensity uptake is also documented in the literature but was found to be unsuitable for the purpose of head and neck GTV contouring. Systems based on the physical features of the PET-CT images are also discussed and are found to produce very promising results.

Conclusion: A number of quantitative techniques are evaluated and currently the most suitable is found to be SBR, however even this method was not found to be entirely reliable. More promising techniques need further evaluation before they could be implemented clinically and a Radiation Oncologist or Nuclear Medicine Radiologist must still validate all GTVs produced by quantitative methods.

Type
Literature Review
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Fletcher, JW, Djulbegovic, B, Soares, HPet al. Recommendations on the use of 18F-FDG PET in oncology. J Nucl Med 2008; 49:480508.CrossRefGoogle ScholarPubMed
Troost, EG, Schinagl, DA, Bussink, J, Oyen, WJ, Kaanders, JH. Clinical evidence on PET-CT for radiation therapy planning in head and neck tumours. Radiother Oncol 2010; 96:328334.CrossRefGoogle ScholarPubMed
Højgaard, L, Specht, L. PET/CT in head and neck cancer. Eur J Nucl Med Mol Imaging 2007; 34:13291333.CrossRefGoogle ScholarPubMed
Breen, SL, Publicover, J, De Silva, Set al. Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers. Int J Radiat Oncol Biol Phys 2007; 68:763770.CrossRefGoogle ScholarPubMed
Riegel, AC, Berson, AM, Destian, Set al. Variability of gross tumor volume delineation in head-and-neck cancer using CT and PET/CT fusion. Int J Radiat Oncol Biol Phys 2006; 65:726732.CrossRefGoogle ScholarPubMed
Lee, JA. Segmentation of positron emission tomography images: some recommendations for target delineation in radiation oncology. Radiother Oncol 2010; 96:302307.CrossRefGoogle ScholarPubMed
Rock, L., Chief Physicist, UPMC Beacon Hospital, Dublin. January 11, 2012, personal communication.Google Scholar
Zaidi, H, El Naqa, I. PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur J Nucl Med Mol Imaging 2010; 37:21652187.CrossRefGoogle ScholarPubMed
Thie, JA. Understanding the standardized uptake value, its methods, and implications for usage. J Nucl Med 2004; 45:14311434.Google ScholarPubMed
Keyes, JW Jr. SUV: standard uptake or silly useless value? J Nucl Med 1995; 36:18361839.Google ScholarPubMed
Lindholm, P, Minn, H, Leskinen-Kallio, Set al. Influence of the blood glucose concentration on FDG uptake in cancer–a PET study. J Nucl Med 1993; 34:16.Google ScholarPubMed
Hustinx, R, Smith, RJ, Benard, Fet al. Dual time point fluorine-18 fluorodeoxyglucose positron emission tomography: a potential method to differentiate malignancy from inflammation and normal tissue in the head and neck. Eur J Nucl Med 1999; 26:13451348.CrossRefGoogle ScholarPubMed
Daisne, JF, Sibomana, M, Bol, Aet al. Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms. Radiother Oncol 2003; 69:247250.CrossRefGoogle ScholarPubMed
Van Baardwijk, A, Bosmans, G, Boersma, Let al. PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes. Int J Radiat Oncol Biol Phys 2007; 68:771778.CrossRefGoogle ScholarPubMed
Daisne, JF, Duprez, T, Weynand, Bet al. Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. Radiology 2004; 233:93100.CrossRefGoogle ScholarPubMed
Davis, JB, Reiner, B, Huser, Met al. Assessment of 18F PET signals for automatic target volume definition in radiotherapy treatment planning. Radiother Oncol 2006; 80:4350.CrossRefGoogle ScholarPubMed
Wanet, M, Lee, JA, Weynand, Bet al. Gradient-based delineation of the primary GTV on FDG-PET in non-small cell lung cancer: a comparison with threshold-based approaches, CT and surgical specimens. Radiother Oncol 2011; 98:117125.CrossRefGoogle ScholarPubMed
Nestle, U, Schaefer-Schuler, A, Kremp, Set al. Target volume definition for 18F-FDG PET-positive lymph nodes in radiotherapy of patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging 2007; 34:453462.CrossRefGoogle ScholarPubMed
Nestle, U, Kremp, S, Schaefer-Schuler, Aet al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer. J Nucl Med 2005; 46:13421348.Google ScholarPubMed
Geets, X, Lee, JA, Bol, A, Lonneux, M, Grégoire, V. A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging 2007; 34:14271438.CrossRefGoogle ScholarPubMed
Yu, H, Caldwell, C, Mah, Ket al. Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images. Int J Radiat Oncol Biol Phys 2009; 75:618625.CrossRefGoogle ScholarPubMed
Schinagl, DA, Vogel, WV, Hoffmann, ALet al. Comparison of five segmentation tools for 18F-fluoro-deoxy-glucose-positron emission tomography-based target volume definition in head and neck cancer. Int J Radiat Oncol Biol Phys 2007; 69:12821289.CrossRefGoogle ScholarPubMed
Schinagl, DA, Hoffmann, AL, Vogel, WVet al. Can FDG-PET assist in radiotherapy target volume definition of metastatic lymph nodes in head-and-neck cancer? Radiother Oncol 2009; 91:95100.CrossRefGoogle ScholarPubMed