Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Molecular Imaging and Biology
Published in: Molecular Imaging and Biology 1/2019

Open Access 01-02-2019 | Research Article

Performance Evaluation of a Semi-automated Method for [18F]FDG Uptake in Abdominal Visceral Adipose Tissue

Authors: Stefanie A. de Boer, Daan S. Spoor, Riemer H. J. A. Slart, Douwe J. Mulder, Melanie Reijrink, Ronald J. H. Borra, Gerbrand M. Kramer, Otto S. Hoekstra, Ronald Boellaard, Marcel J. Greuter

Published in: Molecular Imaging and Biology | Issue 1/2019

Login to get access

Abstract

Purpose

Severity of abdominal obesity and possibly levels of metabolic activity of abdominal visceral adipose tissue (VAT) are associated with an increased risk for cardiovascular disease (CVD). In this context, the purpose of the current study was to evaluate the reproducibility and repeatability of a semi-automated method for assessment of the metabolic activity of VAT using 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET)/x-ray computed tomography (CT).

Procedures

Ten patients with lung cancer who underwent two baseline whole-body [18F]FDG PET/low-dose (LD) CT scans within 1 week were included. Abdominal VAT was automatically segmented using CT between levels L1–L5. The initial CT-based segmentation was further optimized using PET data with a standardized uptake value (SUV) threshold approach (range 1.0–2.5) and morphological erosion (range 0–5 pixels). The [18F]FDG uptake in SUV that was measured by the automated method was compared with manual analysis. The reproducibility and repeatability were quantified using intraclass correlation coefficients (ICCs).

Results

The metabolic assessment of VAT on [18F]FDG PET/LDCT scans expressed as SUVmean, using an automated method showed high inter and intra observer (all ICCs > 0.99) and overall repeatability (ICC = 0.98). The manual method showed reproducible inter observer (all ICCs > 0.92), but less intra observer (ICC = 0.57) and less overall repeatability (ICC = 0.78) compared with the automated method.

Conclusions

Our proposed semi-automated method provided reproducible and repeatable quantitative analysis of [18F]FDG uptake in VAT. We expect this method to aid future research regarding the role of VAT in development of CVD.
Appendix
Available only for authorised users
Literature
1.
2.
3.
Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D'Agostino RB, O'Donnell CJ (2007) Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation 116:39–48CrossRefPubMed
4.
Messier V, Karelis AD, Prud'homme D, Primeau V, Brochu M, Rabasa-Lhoret R (2010) Identifying metabolically healthy but obese individuals in sedentary postmenopausal women. Obesity (Silver Spring) 18:911–917CrossRef
5.
Wildman RP, Muntner P, Reynolds K, McGinn A, Rajpathak S, Wylie-Rosett J, Sowers MR (2008) The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med 168:1617–1624CrossRefPubMed
6.
Wajchenberg BL (2000) Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev 21:697–738CrossRefPubMed
7.
Lee JJ, Pedley A, Hoffmann U, Massaro JM, Fox CS (2016) Association of changes in abdominal fat quantity and quality with incident cardiovascular disease risk factors. J Am Coll Cardiol 68:1509–1521CrossRefPubMedPubMedCentral
8.
Lau DC, Dhillon B, Yan H et al (2005) Adipokines: molecular links between obesity and atheroslcerosis. Am J Physiol Heart Circ Physiol 288:H2031–H2041CrossRefPubMed
9.
Rosenquist KJ, Pedley A, Massaro JM, Therkelsen KE, Murabito JM, Hoffmann U, Fox CS (2013) Visceral and subcutaneous fat quality and cardiometabolic risk. JACC Cardiovasc Imaging 6:762–771CrossRefPubMedPubMedCentral
10.
Calabro P, Golia E, Maddaloni V et al (2009) Adipose tissue-mediated inflammation: the missing link between obesity and cardiovascular disease? Intern Emerg Med 4:25–34CrossRefPubMed
11.
Lee JJ, Pedley A, Hoffmann U, Massaro JM, Keaney JF Jr, Vasan RS, Fox CS (2016) Cross-sectional associations of computed tomography (CT)-derived adipose tissue density and Adipokines: the Framingham Heart Study. J Am Heart Assoc 5:e002545PubMedPubMedCentral
12.
Apovian CM, Bigornia S, Mott M, Meyers MR, Ulloor J, Gagua M, McDonnell M, Hess D, Joseph L, Gokce N (2008) Adipose macrophage infiltration is associated with insulin resistance and vascular endothelial dysfunction in obese subjects. Arterioscler Thromb Vasc Biol 28:1654–1659CrossRefPubMedPubMedCentral
13.
Ohman MK, Shen Y, Obimba CI, Wright AP, Warnock M, Lawrence DA, Eitzman DT (2008) Visceral adipose tissue inflammation accelerates atherosclerosis in apolipoprotein E-deficient mice. Circulation 117:798–805CrossRefPubMedPubMedCentral
14.
Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, Ferrante AW Jr (2003) Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest 112:1796–1808CrossRefPubMedPubMedCentral
15.
Bucerius J, Mani V, Wong S, Moncrieff C, Izquierdo-Garcia D, Machac J, Fuster V, Farkouh ME, Rudd JHF, Fayad ZA (2014) Arterial and fat tissue inflammation are highly correlated: a prospective [18F]FDG PET/CT study. Eur J Nucl Med Mol Imaging 41:934–945CrossRefPubMedPubMedCentral
16.
17.
Elkhawad M, Rudd JHF, Sarov-Blat L, Cai G, Wells R, Davies LC, Collier DJ, Marber MS, Choudhury RP, Fayad ZA, Tawakol A, Gleeson FV, Lepore JJ, Davis B, Willette RN, Wilkinson IB, Sprecher DL, Cheriyan J (2012) Effects of p38 mitogen-activated protein kinase inhibition on vascular and systemic inflammation in patients with atherosclerosis. JACC Cardiovasc Imaging 5:911–922CrossRefPubMed
18.
Christen T, Sheikine Y, Rocha VZ, Hurwitz S, Goldfine AB, di Carli M, Libby P (2010) Increased glucose uptake in visceral versus subcutaneous adipose tissue revealed by PET imaging. JACC Cardiovasc Imaging 3:843–851CrossRefPubMedPubMedCentral
19.
Vanfleteren LEGW, Van Meerendonk AMG, Franssen FM et al (2014) A possible link between increased metabolic activity of fat tissue and aortic wall inflammation in subjects with COPD. A retrospective [18F]FDG-PET/CT pilot study. Respir Med 108:883–890CrossRefPubMed
20.
Oliveira AL, Azevedo DC, Bredella MA, Stanley TL, Torriani M (2015) Visceral and subcutaneous adipose tissue FDG uptake by PET/CT in metabolically healthy obese subjects. Obesity 23:286–289CrossRefPubMed
21.
Tahara N, Yamagishi S, Kodama N, Tahara A, Honda A, Nitta Y, Igata S, Matsui T, Takeuchi M, Kaida H, Kurata S, Abe T, Fukumoto Y (2015) Clinical and biochemical factors associated with area and metabolic activity in the visceral and subcutaneous adipose tissues by FDG-PET/CT. J Clin Endocrinol Metab 100:E739–E747CrossRefPubMed
22.
Bucerius J, Vijgen GH, Brans B et al (2015) Impact of bariatric surgery on carotid artery inflammation and the metabolic activity in different adipose tissues. Medicine (Baltimore) 94:e725CrossRef
23.
Torigian DA, Green-McKenzie J, Liu X, Shofer FS, Werner T, Smith CE, Strasser AA, Moghbel MC, Parekh AH, Choi G, Goncalves MD, Spaccarelli N, Gholami S, Kumar PS, Tong Y, Udupa JK, Mesaros C, Alavi A (2017) A study of the feasibility of FDG-PET/CT to systematically detect and quantify differential metabolic effects of chronic tobacco use in organs of the whole body-a prospective pilot study. Acad Radiol 24:930–940CrossRefPubMed
24.
Van de Wiele C, Van Vlaenderen M, D’Hulst L et al (2017) Metabolic and morphological measurements of subcutaneous and visceral fat and their relationship with disease stage and overall survival in newly diagnosed pancreatic adenocarcinoma: metabolic and morphological fat measurements in pancreatic adenocarcinoma. Eur J Nucl Med Mol Imaging 44:110–116CrossRefPubMed
25.
Veld J, O’Donnell EK, Reagan MR, Yee AJ, Torriani M, Rosen CJ, Bredella MA (2016) Abdominal adipose tissue in MGUS and multiple myeloma. Skelet Radiol 45:1277–1283CrossRef
26.
Pahk K, Rhee S, Kim S, Choe JG (2016) Predictive role of functional visceral fat activity assessed by preoperative F-18 FDG PET/CT for regional lymph node or distant metastasis in patients with colorectal cancer. PLoS One 11:e0148776CrossRefPubMedPubMedCentral
27.
Im HJ, Paeng JC, Cheon GJ, Kim EE, Lee JS, Goo JM, Kang KW, Chung JK, Lee DS (2016) Feasibility of simultaneous [18F]FDG PET/MRI for the quantitative volumetric and metabolic measurements of abdominal fat tissues using fat segmentation. Nucl Med Commun 37:616–622CrossRefPubMed
28.
Kodama N, Tahara N, Tahara A, Honda A, Nitta Y, Mizoguchi M, Kaida H, Ishibashi M, Abe T, Ikeda H, Narula J, Fukumoto Y, Yamagishi SI, Imaizumi T (2013) Effects of pioglitazone on visceral fat metabolic activity in impaired glucose tolerance or type 2 diabetes mellitus. J Clin Endocrinol Metab 98:4438–4445CrossRefPubMed
29.
Reichkendler MH, Auerbach P, Rosenkilde M et al (2013) Exercise training favors increased insulin-stimulated glucose uptake in skeletal muscle in contrast to adipose tissue: a randomized study using FDG PET imaging. Am J Physiol Endocrinol Metab 305:E496–E506CrossRefPubMed
30.
Kramer GM, Frings V, Hoetjes N, Hoekstra OS, Smit EF, de Langen AJ, Boellaard R (2016) Repeatability of quantitative whole-body [18F]FDG PET/CT uptake measures as function of uptake interval and lesion selection in non-small cell lung cancer patients. J Nucl Med 57:1343–1349CrossRefPubMed
31.
Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, Verzijlbergen FJ, Barrington SF, Pike LC, Weber WA, Stroobants S, Delbeke D, Donohoe KJ, Holbrook S, Graham MM, Testanera G, Hoekstra OS, Zijlstra J, Visser E, Hoekstra CJ, Pruim J, Willemsen A, Arends B, Kotzerke J, Bockisch A, Beyer T, Chiti A, Krause BJ, European Association of Nuclear Medicine (EANM) (2015) FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 42:328–354CrossRefPubMed
32.
Figueroa AL, Takx RA, MacNabb MH et al (2016) Relationship between measures of adiposity, arterial inflammation, and subsequent cardiovascular events. Circ Cardiovasc Imaging 9:e004043CrossRefPubMedPubMedCentral
33.
Maurovich-Horvat P, Massaro J, Fox CS, Moselewski F, O'Donnell CJ, Hoffmann U (2007) Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomography. Int J Obes 31:500–506CrossRef
34.
Shuster A, Patlas M, Pinthus JH, Mourtzakis M (2012) The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. Br J Radiol 85:1–10CrossRefPubMedPubMedCentral
35.
Nemoto M, Yeernuer T, Masutani Y et al (2014) Development of automatic visceral fat volume calculation software for CT volume data. J Obes 2014:95084CrossRef
36.
Kinahan PE, Fletcher JW (2010) Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. Semin Ultrasound CT MR 31:496–505CrossRefPubMedPubMedCentral
37.
Bland JM, Altman DG (2007) Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat 17:571–582CrossRefPubMed
38.
39.
Goodpaster BH, Krishnaswami S, Harris TB, Katsiaras A, Kritchevsky SB, Simonsick EM, Nevitt M, Holvoet P, Newman AB (2005) Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med 165:777–783CrossRefPubMed
Metadata
Title
Performance Evaluation of a Semi-automated Method for [18F]FDG Uptake in Abdominal Visceral Adipose Tissue
Authors
Stefanie A. de Boer
Daan S. Spoor
Riemer H. J. A. Slart
Douwe J. Mulder
Melanie Reijrink
Ronald J. H. Borra
Gerbrand M. Kramer
Otto S. Hoekstra
Ronald Boellaard
Marcel J. Greuter
Publication date
01-02-2019
Publisher
Springer International Publishing
Published in
Molecular Imaging and Biology / Issue 1/2019
Print ISSN: 1536-1632
Electronic ISSN: 1860-2002
DOI
https://doi.org/10.1007/s11307-018-1211-1

Other articles of this Issue 1/2019

Molecular Imaging and Biology 1/2019 Go to the issue

Research Article

Preclinical Evaluation of a Novel TSPO PET Ligand 2-(7-Butyl-2-(4-(2-[18F]Fluoroethoxy)phenyl)-5-Methylpyrazolo[1,5-a]Pyrimidin-3-yl)-N,N-Diethylacetamide (18F-VUIIS1018A) to Image Glioma

Research Article

Zero-Extra-Dose PET Delayed Imaging with Data-Driven Attenuation Correction Estimation

Research Article

Near-infrared Fluorescence Ocular Imaging (NIRFOI) of Alzheimer’s Disease

Research Article

The Value of In Vitro Binding as Predictor of In Vivo Results: A Case for [18F]FDDNP PET

Research Article

Spatial Patterns of Hypometabolism and Amyloid Deposition in Variants of Alzheimer’s Disease Corresponding to Brain Networks: a Prospective Cohort Study

Research Article

Site-Specific Fluorescent Labeling of Antibodies and Diabodies Using SpyTag/SpyCatcher System for In Vivo Optical Imaging