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

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
EJNMMI Research
Published in: EJNMMI Research 1/2018

Open Access 01-12-2018 | Original research

Comparison of three-parameter kinetic model analysis to standard Patlak’s analysis in 18F-FDG PET imaging of lung cancer patients

Authors: E. Laffon, M. L. Calcagni, G. Galli, A. Giordano, A. Capotosti, R. Marthan, L. Indovina

Published in: EJNMMI Research | Issue 1/2018

Login to get access

Abstract

Background

Patlak’s graphical analysis can provide tracer net influx constant (Ki) with limitation of assuming irreversible tracer trapping, that is, release rate constant (kb) set to zero. We compared linear Patlak’s analysis to non-linear three-compartment three-parameter kinetic model analysis (3P-KMA) providing Ki, kb, and fraction of free 18F-FDG in blood and interstitial volume (Vb).

Methods

Dynamic PET data of 21 lung cancer patients were retrospectively analyzed, yielding for each patient an 18F-FDG input function (IF) and a tissue time-activity curve. The former was fitted with a three-exponentially decreasing function, and the latter was fitted with an analytical formula involving the fitted IF data (11 data points, ranging 7.5–57.5 min post-injection). Bland-Altman analysis was used for Ki comparison between Patlak’s analysis and 3P-KMA. Additionally, a three-compartment five-parameter KMA (5P-KMA) was implemented for comparison with Patlak’s analysis and 3P-KMA.

Results

We found that 3P-KMA Ki was significantly greater than Patlak’s Ki over the whole patient series, + 6.0% on average, with limits of agreement of ± 17.1% (95% confidence). Excluding 8 out of 21 patients with kb > 0 deleted this difference. A strong correlation was found between Ki ratio (=3P-KMA/Patlak) and kb (R = 0.801; P < 0.001). No significant difference in Ki was found between 3P-KMA versus 5P-KMA, and between 5P-KMA versus Patlak’s analysis, with limits of agreement of ± 23.0 and ± 31.7% (95% confidence), respectively.

Conclusions

Comparison between 3P-KMA and Patlak’s analysis significantly showed that the latter underestimates Ki because it arbitrarily set kb to zero: the greater the kb value, the greater the Ki underestimation. This underestimation was not revealed when comparing 5P-KMA and Patlak’s analysis. We suggest that further studies are warranted to investigate the 3P-KMA efficiency in various tissues showing greater 18F-FDG trapping reversibility than lung cancer lesions.
Literature
1.
Som P, Atkins HL, Bandoypadhyay D, et al. A fluorinated glucose analog, 2-fluoro-2-deoxy-d-glucose (F-18): nontoxic tracer for rapid tumor detection. J Nucl Med. 1980;21:670–5.PubMed
2.
3.
Galli G, Indovina L, Calcagni ML, Mansi L, Giordano A. The quantification with FDG as seen by a physician. Nucl Med Biol. 2013;40:720–30.CrossRef
4.
Laffon E, de Clermont H, Begueret H, Vernejoux J-M, Thumerel M, Marthan R, Ducassou D. Assessment of dual time point 18F-FDG imaging for pulmonary lesions. Nucl Med Commun. 2009;30:455–61.CrossRef
5.
Sokoloff L, Reivich M, Kennedy C, et al. The |14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat J Neurochem 1977;28:897–916.
6.
Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-Fluoro-2-deoxy-d-glucose: validation of method. Ann Neuro. 1979;6:371–88.CrossRef
7.
Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983;3:1–7.CrossRef
8.
Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data: generalizations. J Cereb Blood Flow Metab. 1985;5:584–90.CrossRef
9.
Okazumi S, Isono K, Enomoto K, et al. Evaluation of liver tumors using fluorine-18-fluorodeoxyglucose PET: characterization of tumor and assessment of effect of treatment. J Nucl Med. 1992;33:333–9.PubMed
10.
Torizuka T, Tamaki N, Inokuma T, et al. In vivo assessment of glucose metabolism in hepatocellular carcinoma with FDG-PET. J Nucl Med. 1995;36:1811–7.PubMed
11.
Dimitrakopoulou-Strauss A, Georgoulias V, Eisenhut M, et al. Quantitative assessment of SSTR2 expression in patients with non-small cell lung cancer using 68Ga-DOTATOC PET and comparison with 18F-FDG PET. Eur J Nucl Med Mol Imaging. 2006;33:823–30.CrossRef
12.
Laffon E, de Clermont H, Vernejoux JM, Jougon J, Marthan R. Feasibility of assessing [18F]FDG lung metabolism with late dynamic PET imaging. Mol Imaging Biol. 2011;13:378–84.CrossRef
13.
Calcagni ML, Indovina L, Di Franco D, et al. Are the simplified methods to estimate Ki in 18F-FDG PET studies feasible in clinical routine? Comparison between three simplified methods. Q J Nucl Med Mol Imaging. 2014;5 [Epub ahead of print]
14.
Lammertsma AA. Forward to the past: the case for quantitative PET imaging. J Nucl Med. 2017;58:1019–24.CrossRef
15.
Laffon E, Adhoute X, de Clermont H, Marthan R. Is liver SUV stable over time in 18F-FDG PET imaging? J Nucl Med Technol. 2011;39:1–6.CrossRef
16.
Hunter GJ, Hamberg LM, Alpert NM, Choi NC, Fischman AJ. Simplified measurement of deoxyglucose utilization rate. J Nucl Med. 1996;37:950–5.PubMed
17.
Vriens D, de Geus-Oei L-F, Oyen WJG, Visser EP. A curve-fitting approach to estimate the arterial plasma input function for the assessment of glucose metabolic rate and response to treatment. J Nucl Med. 2009;50:1933–9.CrossRef
18.
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10.CrossRef
19.
de Geus-Oei LF, Visser EP, Krabbe PF, et al. Comparison of image-derived and arterial input functions for estimating the rate of glucose metabolism in therapy-monitoring18F-FDG PET studies. J Nucl Med. 2006;47:945–9.PubMed
20.
JCGM 100: 2008 . Evaluation of measurement data—guide to the expression of uncertainty in measurement; 2008. Available at: http://​www.​bipm.​org. [Accessed September 2008].
Metadata
Title
Comparison of three-parameter kinetic model analysis to standard Patlak’s analysis in 18F-FDG PET imaging of lung cancer patients
Authors
E. Laffon
M. L. Calcagni
G. Galli
A. Giordano
A. Capotosti
R. Marthan
L. Indovina
Publication date
01-12-2018
Publisher
Springer Berlin Heidelberg
Published in
EJNMMI Research / Issue 1/2018
Electronic ISSN: 2191-219X
DOI
https://doi.org/10.1186/s13550-018-0369-5

Other articles of this Issue 1/2018

EJNMMI Research 1/2018 Go to the issue

Original Research

Evaluation of hepatic integrin αvβ3 expression in non-alcoholic steatohepatitis (NASH) model mouse by 18F-FPP-RGD2 PET

Original research

[11C]SCH23390 binding to the D1-dopamine receptor in the human brain—a comparison of manual and automated methods for image analysis

Original research

Feasibility of biventricular volume and function assessment using first-pass gated 15O-water PET

Original research

Validation of [18F]FLT as a perfusion-independent imaging biomarker of tumour response in EGFR-mutated NSCLC patients undergoing treatment with an EGFR tyrosine kinase inhibitor

Original research

Optimization of temporal sampling for 18F-choline uptake quantification in prostate cancer assessment

Original research

Biokinetic and dosimetric aspects of 64CuCl2 in human prostate cancer: possible theranostic implications