Reevaluation of the Standardized Uptake Value for FDG: Variations with Body Weight and Methods for Correction

Sugawara, Yumi; Zasadny, Kenneth R.; Neuhoff, A; Wahl, Richard L.

doi:10.1148/radiology.213.2.r99nv37521

Cited by 223 publications

(158 citation statements)

References 13 publications

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“…Impaired glucose metabolism or diabetes can affect FDG uptake and decrease PET sensitivity in detecting smaller lesions (22)(23)(24). Patients with a large volume of body fat may alter FDG uptake in normal tissues and the tumor (25,26). Other factors that can influence FDG bio-distribution include steroids, sedative use to induce muscle relaxation during scanning, and the patient's hydration status (18).…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value of Metabolic Tumor Volume and Velocity in Predicting Head and Neck Cancer Outcomes

Chu¹,

Murphy²,

La³

et al. 2010

International Journal of Radiation Oncology*Biology*Physics

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Purpose-We previously showed that metabolic tumor volume (MTV) on PET-CT predicts for disease recurrence and death in head and neck cancer (HNC). We hypothesized that increases in MTV over time would correlate with tumor growth and biology, and predict outcome. We sought to examine tumor growth over time in serial pre-treatment PET-CT scans. [2006][2007][2008][2009] 51 patients had two PET-CT scans prior to HNC treatment. MTV was defined as the tumor volume ≥50% of maximum SUV (SUV max ). MTV was calculated for the primary tumor, nodal disease, and composite (primary tumor + nodes). MTV and SUV velocity were defined as the change in MTV or SUV max over time respectively. Cox regression analyses were used to examine correlations between SUV, MTV velocity, and outcome (disease progression and overall survival [OS]). Methods and Materials-FromResults-Median follow-up time was 17.5 months. Median time between PET-CT scans was 3 weeks. Unexpectedly, 51% of cases demonstrated a decrease in SUV max (average −0.1cc/week) and MTV (average −0.3cc/week) over time. Despite the variability in MTV, primary tumor MTV velocity predicted disease progression (hazard ratio [HR] 2.94; p=0.01), and OS (HR 1.85; p=0.03). Conclusions-Primarytumor MTV velocity appears to be a better prognostic indicator of disease progression and survival compared to nodal MTV velocity. However, substantial variability was found in PET-CT biomarkers between serial scans. Caution should be used when integrating PET-CT biomarkers into clinical protocols in HNC.Keywords metabolic tumor volume; functional imaging; head and neck cancers; PETCT

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Section: Discussionmentioning

confidence: 99%

Prognostic Value of Metabolic Tumor Volume and Velocity in Predicting Head and Neck Cancer Outcomes

Chu¹,

Murphy²,

La³

et al. 2010

International Journal of Radiation Oncology*Biology*Physics

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“…For FDG-PET the SUV max was corrected for lean body mass (SUL max ) in order to exclude any influence of body shape. 19,24 Each SUL max for FDG-PET or SUV max for MET-PET was analyzed based on the pathological findings after surgery. The diagnostic validity of SUL max or SUV max was also compared with the tumor proliferation index using an MIB-1 monoclonal antibody (MIB-1 index).…”

Section: Pet Analysismentioning

confidence: 99%

Qualitative analysis of spinal intramedullary lesions using PET/CT

Naito

Yamagata

Arima³

et al. 2015

SPI

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OBJECT Although the usefulness of PET for brain lesions has been established, few reports have examined the use of PET for spinal intramedullary lesions. This study investigated the diagnostic utility of PET/CT for spinal intramedullary lesions. METHODS l-[methyl-11C]-methionine (MET)- or [18F]-fluorodeoxyglucose (FDG)-PET/CT was performed in 26 patients with spinal intramedullary lesions. The region of interest (ROI) within the spinal cord parenchyma was placed manually in the axial plane. Maximum pixel counts in the ROIs were normalized to the maximum standardized uptake value (SUVmax) using subject body weight. For FDG-PET the SUVmax was corrected for lean body mass (SULmax) to exclude any influence of the patient’s body shape. Each SUV was analyzed based on histopathological results after surgery. The diagnostic validity of the SUV was further compared with the tumor proliferation index using the MIB-1 monoclonal antibody (MIB-1 index). RESULTS A total of 16 patients underwent both FDG-PET and MET-PET, and the remaining 10 patients underwent either FDG-PET or MET-PET. Pathological diagnoses included high-grade malignancy such as glioblastoma multiforme, anaplastic astrocytoma, or anaplastic ependymoma in 5 patients; low-grade malignancy such as hemangioblastoma, diffuse astrocytoma, or ependymoma in 12 patients; and nonneoplastic lesion including cavernous malformation in 9 patients. Both FDG and MET accumulated significantly in high-grade malignancy, and the SULmax and SUVmax correlated with the tumor proliferation index. Therapeutic response after chemotherapy or radiation in high-grade malignancy was well monitored. However, a significant difference in SULmax and SUVmax for FDG-PET and MET-PET was not evident between low-grade malignancy and nonneoplastic lesions. CONCLUSIONS Spinal PET/CT using FDG or MET for spinal intramedullary lesions appears useful and practical, particularly for tumors with high-grade malignancy. Differentiation of tumors with low-grade malignancy from nonneoplastic lesions may still prove difficult. Further technological refinement, including the selection of radiotracer or analysis evaluation methods, is needed.

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“…Although there are several reports that present lymph node detection [3,4], these methods detect lymph nodes based on SUV [7] primary from PET images or detect in 2-D image processing way. There is no method that detects lymph nodes directly from 3-D images.…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Lymph Nodes from 3-D Abdominal CT Images Using 3-D Minimum Directional Difference Filter

Kitasaka

Tsujimura

Nakamura

et al.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

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Abstract. This paper presents a method for extracting lymph node regions from 3-D abdominal CT images using 3-D minimum directional difference filter. In the case of surgery of colonic cancer, resection of metastasis lesions is performed with resection of a primary lesion. Lymph nodes are main route of metastasis and are quite important for deciding resection area. Diagnosis of enlarged lymph nodes is quite important process for surgical planning. However, manual detection of enlarged lymph nodes on CT images is quite burden task. Thus, development of lymph node detection process is very helpful for assisting such surgical planning task. Although there are several report that present lymph node detection, these methods detect lymph nodes primary from PET images or detect in 2-D image processing way. There is no method that detects lymph nodes directly from 3-D images. The purpose of this paper is to show an automated method for detecting lymph nodes from 3-D abdominal CT images. This method employs a 3-D minimum directional difference filter for enhancing blob structures with suppressing line structures. After that, false positive regions caused by residua and vein are eliminated using several kinds of information such as size, blood vessels, air in the colon. We applied the proposed method to three cases of 3-D abdominal CT images. The experimental results showed that the proposed method could detect 57.0 % of enlarged lymph nodes with 58 FPs per case.

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Reevaluation of the Standardized Uptake Value for FDG: Variations with Body Weight and Methods for Correction

Abstract: SUVlbm and SUVbsa are weight-independent indices for FDG uptake, and SUVlbm appears to be more appropriate for quantifying FDG uptake to avoid overestimation of glucose utilization in obese patients.

Cited by 223 publications

References 13 publications

Prognostic Value of Metabolic Tumor Volume and Velocity in Predicting Head and Neck Cancer Outcomes

Prognostic Value of Metabolic Tumor Volume and Velocity in Predicting Head and Neck Cancer Outcomes

Qualitative analysis of spinal intramedullary lesions using PET/CT

Automated Extraction of Lymph Nodes from 3-D Abdominal CT Images Using 3-D Minimum Directional Difference Filter

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