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Review

Mathematical Models for FDG Kinetics in Cancer: A Review

1
Life Science Computational Laboratory (LISCOMP), Largo Rosanna Benzi 10, 16132 Genova, Italy
2
Dipartimento di Matematica, Università di Genova, Via Dodecaneso 35, 16146 Genova, Italy
3
CNR-SPIN, Corso Perrone 24, 16152 Genova, Italy
4
Dipartimento di Scienze della Salute, Università di Genova, Largo Rosanna Benzi 10, 16132 Genova, Italy
5
Ospedale Policlinico San Martino IRCCS, Largo Rosanna Benzi 10, 16132 Genova, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Seongho Kim
Metabolites 2021, 11(8), 519; https://doi.org/10.3390/metabo11080519
Received: 27 June 2021 / Revised: 28 July 2021 / Accepted: 2 August 2021 / Published: 6 August 2021
(This article belongs to the Special Issue Data Science in Metabolomics)
Compartmental analysis is the mathematical framework for the modelling of tracer kinetics in dynamical Positron Emission Tomography. This paper provides a review of how compartmental models are constructed and numerically optimized. Specific focus is given on the identifiability and sensitivity issues and on the impact of complex physiological conditions on the mathematical properties of the models. View Full-Text
Keywords: Positron Emission Tomography (PET); 2-deoxy-2-[18F]fluoro-D-glucose (FDG); tracer kinetics; compartmental analysis Positron Emission Tomography (PET); 2-deoxy-2-[18F]fluoro-D-glucose (FDG); tracer kinetics; compartmental analysis
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MDPI and ACS Style

Sommariva, S.; Caviglia, G.; Sambuceti, G.; Piana, M. Mathematical Models for FDG Kinetics in Cancer: A Review. Metabolites 2021, 11, 519. https://doi.org/10.3390/metabo11080519

AMA Style

Sommariva S, Caviglia G, Sambuceti G, Piana M. Mathematical Models for FDG Kinetics in Cancer: A Review. Metabolites. 2021; 11(8):519. https://doi.org/10.3390/metabo11080519

Chicago/Turabian Style

Sommariva, Sara, Giacomo Caviglia, Gianmario Sambuceti, and Michele Piana. 2021. "Mathematical Models for FDG Kinetics in Cancer: A Review" Metabolites 11, no. 8: 519. https://doi.org/10.3390/metabo11080519

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