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Measurement of metabolic tumor volume: static versus dynamic FDG scans.

Cheebsumon P, van Velden FH, Yaqub M, Hoekstra CJ, Velasquez LM, Hayes W, Hoekstra OS, Lammertsma AA, Boellaard R - EJNMMI Res (2011)

Bottom Line: Dynamic [18F]-fluoro-2-deoxy-D-glucose [FDG] PET data from 10 lung and 8 gastrointestinal cancer patients were analyzed retrospectively.These differences depend strongly on the delineation method used.Delineation methods that correct for local SBR provide the most consistent results between SUV and Patlak images.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Nuclear Medicine & PET Research, VU University Medical Center, P,O, Box 7057, Amsterdam, 1007 MB, The Netherlands. r.boellaard@vumc.nl.

ABSTRACT

Background: Metabolic tumor volume assessment using positron-emission tomography [PET] may be of interest for both target volume definition in radiotherapy and monitoring response to therapy. It has been reported, however, that metabolic volumes derived from images of metabolic rate of glucose (generated using Patlak analysis) are smaller than those derived from standardized uptake value [SUV] images. The purpose of this study was to systematically compare metabolic tumor volume assessments derived from SUV and Patlak images using a variety of (semi-)automatic tumor delineation methods in order to identify methods that can be used reliably on (whole body) SUV images.

Methods: Dynamic [18F]-fluoro-2-deoxy-D-glucose [FDG] PET data from 10 lung and 8 gastrointestinal cancer patients were analyzed retrospectively. Metabolic tumor volumes were derived from both Patlak and SUV images using five different types of tumor delineation methods, based on various thresholds or on a gradient.

Results: In general, most tumor delineation methods provided more outliers when metabolic volumes were derived from SUV images rather than Patlak images. Only gradient-based methods showed more outliers for Patlak-based tumor delineation. Median measured metabolic volumes derived from SUV images were larger than those derived from Patlak images (up to 59% difference) when using a fixed percentage threshold method. Tumor volumes agreed reasonably well (< 26% difference) when applying methods that take local signal-to-background ratio [SBR] into account.

Conclusion: Large differences may exist in metabolic volumes derived from static and dynamic FDG image data. These differences depend strongly on the delineation method used. Delineation methods that correct for local SBR provide the most consistent results between SUV and Patlak images.

No MeSH data available.


Related in: MedlinePlus

Coronal images of the measured tumor volumes. Coronal images of the measured tumor volumes derived from SUV and Patlak images of one patient with NSCLC, obtained using four different tumor delineation methods (i.e., VOI50, VOIA50, GradWT1, and GradWT2).
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Figure 1: Coronal images of the measured tumor volumes. Coronal images of the measured tumor volumes derived from SUV and Patlak images of one patient with NSCLC, obtained using four different tumor delineation methods (i.e., VOI50, VOIA50, GradWT1, and GradWT2).

Mentions: In general, measured tumor volumes derived from SUV images were larger than those derived from Patlak images. Example images of the measured tumor volumes derived from SUV and Patlak images are shown in Figure 1. Exceptions were VOIA70 for both types of cancer and the two gradient-based methods for GI cancer (Tables 2 and 3). Large differences (up to 58.7% and 28.1% for NSCLC and GI cancer, respectively) in measured metabolic volume based on the two image types were observed for the various delineation methods (Figure 2A). In the case of NSCLC, the median difference in volume was higher for fixed threshold methods than for adaptive, contrast-oriented, or gradient-based methods. This is further illustrated by Figure 3A, where VOIA50 (i.e., with background correction) shows better correspondence between SUV- and Patlak-based volumes than VOI50 (i.e., without background correction). Only GradWT1 provided no significant difference (p > 0.05) in the metabolic volume derived from SUV and Patlak images, but this may be due to the large spread in differences (Figure 3B). Similar results were found when these differences in volume were compared to SUV (or Ki values, Figures 4A, B). In general, we observed that smaller lesions also had the lowest SUV. Consequently, the largest volume differences between SUV and Patlak image-based tumor delineations were seen for lesions having a low SUV and a small metabolic volume.


Measurement of metabolic tumor volume: static versus dynamic FDG scans.

Cheebsumon P, van Velden FH, Yaqub M, Hoekstra CJ, Velasquez LM, Hayes W, Hoekstra OS, Lammertsma AA, Boellaard R - EJNMMI Res (2011)

Coronal images of the measured tumor volumes. Coronal images of the measured tumor volumes derived from SUV and Patlak images of one patient with NSCLC, obtained using four different tumor delineation methods (i.e., VOI50, VOIA50, GradWT1, and GradWT2).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3285530&req=5

Figure 1: Coronal images of the measured tumor volumes. Coronal images of the measured tumor volumes derived from SUV and Patlak images of one patient with NSCLC, obtained using four different tumor delineation methods (i.e., VOI50, VOIA50, GradWT1, and GradWT2).
Mentions: In general, measured tumor volumes derived from SUV images were larger than those derived from Patlak images. Example images of the measured tumor volumes derived from SUV and Patlak images are shown in Figure 1. Exceptions were VOIA70 for both types of cancer and the two gradient-based methods for GI cancer (Tables 2 and 3). Large differences (up to 58.7% and 28.1% for NSCLC and GI cancer, respectively) in measured metabolic volume based on the two image types were observed for the various delineation methods (Figure 2A). In the case of NSCLC, the median difference in volume was higher for fixed threshold methods than for adaptive, contrast-oriented, or gradient-based methods. This is further illustrated by Figure 3A, where VOIA50 (i.e., with background correction) shows better correspondence between SUV- and Patlak-based volumes than VOI50 (i.e., without background correction). Only GradWT1 provided no significant difference (p > 0.05) in the metabolic volume derived from SUV and Patlak images, but this may be due to the large spread in differences (Figure 3B). Similar results were found when these differences in volume were compared to SUV (or Ki values, Figures 4A, B). In general, we observed that smaller lesions also had the lowest SUV. Consequently, the largest volume differences between SUV and Patlak image-based tumor delineations were seen for lesions having a low SUV and a small metabolic volume.

Bottom Line: Dynamic [18F]-fluoro-2-deoxy-D-glucose [FDG] PET data from 10 lung and 8 gastrointestinal cancer patients were analyzed retrospectively.These differences depend strongly on the delineation method used.Delineation methods that correct for local SBR provide the most consistent results between SUV and Patlak images.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Nuclear Medicine & PET Research, VU University Medical Center, P,O, Box 7057, Amsterdam, 1007 MB, The Netherlands. r.boellaard@vumc.nl.

ABSTRACT

Background: Metabolic tumor volume assessment using positron-emission tomography [PET] may be of interest for both target volume definition in radiotherapy and monitoring response to therapy. It has been reported, however, that metabolic volumes derived from images of metabolic rate of glucose (generated using Patlak analysis) are smaller than those derived from standardized uptake value [SUV] images. The purpose of this study was to systematically compare metabolic tumor volume assessments derived from SUV and Patlak images using a variety of (semi-)automatic tumor delineation methods in order to identify methods that can be used reliably on (whole body) SUV images.

Methods: Dynamic [18F]-fluoro-2-deoxy-D-glucose [FDG] PET data from 10 lung and 8 gastrointestinal cancer patients were analyzed retrospectively. Metabolic tumor volumes were derived from both Patlak and SUV images using five different types of tumor delineation methods, based on various thresholds or on a gradient.

Results: In general, most tumor delineation methods provided more outliers when metabolic volumes were derived from SUV images rather than Patlak images. Only gradient-based methods showed more outliers for Patlak-based tumor delineation. Median measured metabolic volumes derived from SUV images were larger than those derived from Patlak images (up to 59% difference) when using a fixed percentage threshold method. Tumor volumes agreed reasonably well (< 26% difference) when applying methods that take local signal-to-background ratio [SBR] into account.

Conclusion: Large differences may exist in metabolic volumes derived from static and dynamic FDG image data. These differences depend strongly on the delineation method used. Delineation methods that correct for local SBR provide the most consistent results between SUV and Patlak images.

No MeSH data available.


Related in: MedlinePlus