Kinetic modeling of hyperpolarized 13C pyruvate metabolism in tumors using a measured arterial input function

Magnetic Resonance in Medicine

Volumn 70, Issue 4, 2013, Pages 943-953

  Kazan, S M ^a   Reynolds, S ^a   Kennerley, A ^a   Wholey, E ^a,c   Bluff, J E ^a   Berwick, J ^a   Cunningham, V J ^b   Paley, M N ^a   Tozer, G M ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b UNIVERSITY OF ABERDEEN   (United Kingdom)

^c INSTITUTE OF CANCER RESEARCH   (United Kingdom)

Author keywords

arterial input function; dynamic nuclear polarization; hyperpolarization; kinetic modeling

Indexed keywords

ESTIMATION; KINETIC THEORY; KINETICS; MAGNETIC RESONANCE; MAGNETIC RESONANCE SPECTROSCOPY; RATE CONSTANTS; TUMORS;

ARTERIAL INPUT FUNCTION; DIRECT MEASUREMENT; DYNAMIC NUCLEAR POLARIZATION; HYPERPOLARIZATION; HYPERPOLARIZED [1; KINETIC MODELING; PYRUVATE METABOLISM; RELAXATION RATES;

BLOOD;

CARBON 13; LACTIC ACID; PYRUVIC ACID;

ANIMAL EXPERIMENT; ANIMAL MODEL; ARTERIAL BLOOD; ARTICLE; CARDIOVASCULAR PARAMETERS; CLEARANCE; HYPERPOLARIZATION; KINETICS; MALE; MATHEMATICAL MODEL; METABOLISM; NONHUMAN; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; RAT; TUMOR CELL;

ARTERIAL INPUT FUNCTION; DYNAMIC NUCLEAR POLARIZATION; HYPERPOLARIZATION; KINETIC MODELING;

ANIMALS; CARBON ISOTOPES; CELL LINE, TUMOR; COMPUTER SIMULATION; FIBROSARCOMA; LACTIC ACID; MAGNETIC RESONANCE SPECTROSCOPY; MALE; METABOLIC CLEARANCE RATE; MODELS, BIOLOGICAL; PYRUVIC ACID; RADIOPHARMACEUTICALS; RATS;

EID: 84885330774     PISSN: 07403194     EISSN: 15222594     Source Type: Journal    
DOI: 10.1002/mrm.24546     Document Type: Article

References (22)

1. Alymani NA, Smith MD, Williams DJ, Petty RD,. Predictive biomarkers for personalised anti-cancer drug use: discovery to clinical implementation. Eur J Cancer 2010; 46: 869-879.
2. Day SE, Kettunen MI, Gallagher FA, Hu DE, Lerche M, Wolber J, Golman K, Ardenkjaer-Larsen JH, Brindle KM,. Detecting tumor response to treatment using hyperpolarized 13C magnetic resonance imaging and spectroscopy. Nat Med 2007; 13: 1382-1387.
3. Bohndiek SE, Kettunen MI, Hu DE, Brindle KM,. Hyperpolarized 13C spectroscopy detects early changes in tumor vasculature and metabolism after VEGF neutralization. Cancer Res 2012; 72: 854-864.
4. Williamson DH, Lund P, Krebs HA,. The redox state of free nicotinamide-adenine dinucleotide in the cytoplasm and mitochondria of rat liver. Biochem J 1967; 103: 514-527.
5. Warburg O,. On respiratory impairment in cancer cells. Science 1956; 124: 269-270.
6. Chen Z, Lu W, Garcia-Prieto C, Huang P,. The Warburg effect and its cancer therapeutic implications. J Bioenerg Biomembr 2007; 39: 267-274.
7. Herling A, Konig M, Bulik S, Holzhutter HG,. Enzymatic features of the glucose metabolism in tumor cells. FEBS J 2011; 278: 2436-2459.
8. Zierhut ML, Yen YF, Chen AP, Bok R, Albers MJ, Zhang V, Tropp J, Park I, Vigneron DB, Kurhanewicz J, Hurd RE, Nelson SJ,. Kinetic modeling of hyperpolarized 13C1-pyruvate metabolism in normal rats and TRAMP mice. J Magn Reson 2010; 202: 85-92.
9. Albers MJ, Bok R, Chen AP, et al. Hyperpolarized 13C lactate, pyruvate, and alanine: noninvasive biomarkers for prostate cancer detection and grading. Cancer Res 2008; 68: 8607-8615.
10. Spielman DM, Mayer D, Yen YF, Tropp J, Hurd RE, Pfefferbaum A,. In vivo measurement of ethanol metabolism in the rat liver using magnetic resonance spectroscopy of hyperpolarized [1-13C]pyruvate. Magn Reson Med 2009; 62: 307-313.
11. McConnell H,. Reaction rates by nuclear magnetic resonance. J Chem Phys 1958; 28: 430-431.
12. Harris T, Eliyahu G, Frydman L, Degani H,. Kinetics of hyperpolarized 13C1-pyruvate transport and metabolism in living human breast cancer cells. Proc Natl Acad Sci USA 2009; 106: 18131-18136.
13. Workman P, Aboagye EO, Balkwill F, et al. Guidelines for the welfare and use of animals in cancer research. Br J Cancer 2010; 102: 1555-1577.
14. Reynolds S, Kazan S, Bluff J, Port M, Wholey E, Tozer G, Paley M,. Fully MR-compatible syringe pump for the controllable injection of hyperpolarized substrate in animals. Appl Magn Reson 2012;43: 263-273.
15. Nelder J, Mead R,. A simplex method for function minimization. Comp J 1965; 7: 308-313.
16. Akaike H,. Information theory as an extension of the maximum likelihood principle. In 2nd International Symposium on Information Theory, Petrov BN and Csaki F, eds, Budapest: Akademi Kiado; 1973. pp. 267-281.
17. Burnham KP, Anderson DR,. Model selection and inference: a practical information-theoretic approach. New York: Springer; 2003.
18. Brix G, Ziegler SI, Bellemann ME, Doll J, Schosser R, Lucht R, Krieter H, Nosske D, Haberkorn U,. Quantification of [(18)F]FDG uptake in the normal liver using dynamic PET: impact and modeling of the dual hepatic blood supply. J Nucl Med 2001; 42: 1265-1273.
19. Kwong KK, Chesler DA,. Early time points perfusion imaging: theoretical analysis of correction factors for relative cerebral blood flow estimation given local arterial input function. Neuroimage 2011; 57: 182-189.
20. Rausch M, Scheffler K, Rudin M, Radu EW,. Analysis of input functions from different arterial branches with gamma variate functions and cluster analysis for quantitative blood volume measurements. Magn Reson Imaging 2000; 18: 1235-1243.
21. Thompson HK Jr, Starmer CF, Whalen RE, McIntosh HD,. Indicator transit time considered as a gamma variate. Circ Res 1964; 14: 502-515.
22. Kazan SM, Chappell MA, Payne SJ,. Modeling the effects of flow dispersion in arterial spin labeling. IEEE Trans Biomed Eng 2009; 56: 1635-1643.