Modelli compartimentali e cinetica dei radiofarmaci. Teoria dei traccianti, modellistica compartimentale e PET quantitativa
Autori
https://orcid.org/0000-0002-7494-3090
Sinossi
Il volume introduce i principi teorici e applicativi della cinetica dei traccianti radioattivi e dei modelli compartimentali utilizzati in Medicina Nucleare. Dopo aver illustrato i concetti fondamentali relativi ai sistemi biologici, ai traccianti e all'analisi matematica dei modelli, vengono descritti i principali metodi di rappresentazione compartimentale e le relative equazioni differenziali. Una parte significativa del testo è dedicata alla tomografia ad emissione di positroni (PET) con 18F-FDG, alla formulazione matriciale dei modelli, alla trasformata di Laplace, alla convoluzione, all'integrazione numerica e alla derivazione del metodo di Patlak. Il volume include esempi applicativi e script Python per la simulazione e la stima dei parametri cinetici.
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Riferimenti bibliografici
Fondamenti della teoria dei traccianti
1. Hevesy G. The absorption and translocation of lead by plants. Biochem J. 1923;17:439–445.
2. Fick A. Über die Messung des Blutquantums in den Herzventrikeln. Sitzungsberichte der Physikalisch-Medizinischen Gesellschaft zu Würzburg. 1870.
3. Stewart GN. Researches on the circulation time and on the influences which affect it. J Physiol. 1897;22:159–183.
4. Hamilton WF, Moore JW, Kinsman JM, Spurling RG. Simultaneous determination of pulmonary and systemic circulation times. Am J Physiol. 1928;84:338–344.
5. Kety SS, Schmidt CF. The nitrous oxide method for quantitative determination of cerebral blood flow in man. J Clin Invest. 1948;27:476–483.
6. Meier P, Zierler KL. On the theory of indicator-dilution methods for measurement of blood flow and volume. J Appl Physiol. 1954;6:731–744.
7. Zierler KL. Theory of the Use of Tracers in the Study of Body Dynamics. Springfield, IL: Charles C Thomas; 1965.
Modelli compartimentali e analisi cinetica
8. Rescigno A, Segre G. Drug and Tracer Kinetics. Waltham, MA: Blaisdell Publishing; 1966.
9. Shipley RA, Clark RE. Tracer Methods for In Vivo Kinetics. New York: Academic Press; 1972.
10. Atkins GL. Multicompartment Models for Biological Systems. London: Methuen; 1973.
11. Berman M, Weiss MF. SAAM Manual. Washington, DC: U.S. Public Health Service; 1978.
12. Carson ER, Cobelli C, Finkelstein L. The Mathematical Modeling of Metabolic and Endocrine Systems. New York: Wiley; 1983. ISBN: 9780471105662.
13. Jacquez JA. Compartmental Analysis in Biology and Medicine. 3rd ed. Ann Arbor, MI: BioMedware; 1996. ISBN: 9780962992900.
14. Cobelli C, Foster DM, Toffolo G. Tracer Kinetics in Biomedical Research. New York: Springer; 2000. ISBN: 9780306460928.
15. Gunn RN, Gunn SR, Cunningham VJ. Positron emission tomography compartmental models. J Cereb Blood Flow Metab. 2001;21(6):635–652. doi:10.1097/00004647-200106000-00002.
16. Macheras P, Iliadis A. Modeling in Biopharmaceutics, Pharmacokinetics and Pharmacodynamics. New York: Springer; 2006. ISBN: 9780387288978.
17. Endrenyi L, Tothfalusi L. Quantitative Pharmacology. New York: Springer; 2012. ISBN: 9781461424770.
PET quantitativa
18. Phelps ME. PET: Molecular Imaging and Its Biological Applications. New York: Springer; 2004. ISBN: 9780387225270.
19. Bailey DL, Townsend DW, Valk PE, Maisey MN. Positron Emission Tomography: Basic Sciences. London: Springer; 2005. ISBN: 9781846284678.
20. Carson RE. Tracer kinetic modeling in PET. In: Bailey DL, Townsend DW, Valk PE, Maisey MN, editors. Positron Emission Tomography: Basic Sciences. London: Springer; 2005.
21. Rahmim A, Zaidi H. PET versus SPECT: strengths, limitations and challenges. Nucl Med Commun. 2008;29:193–207. doi:10.1097/MNM.0b013e3282f3a515.
22. Cherry SR, Sorenson JA, Phelps ME. Physics in Nuclear Medicine. 4th ed. Philadelphia: Elsevier; 2012. ISBN: 9781416051983.
FDG-PET e metabolismo glucidico
23. Sokoloff L, Reivich M, Kennedy C, et al. The [14C]-deoxyglucose method for the measurement of local cerebral glucose utilization. J Neurochem. 1977;28:897–916.
24. Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomographic measurement of local cerebral glucose metabolic rate in humans with FDG. Ann Neurol. 1979;6:371–388.
25. Huang SC, Phelps ME, Hoffman EJ, et al. Noninvasive determination of local cerebral metabolic rate of glucose in man. Am J Physiol. 1980;238:E69–E82.
26. Reivich M, Alavi A, Wolf A, et al. Glucose metabolic rate kinetic model parameter determination in humans. J Cereb Blood Flow Metab. 1985;5:179–192.
Metodi grafici
27. Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants. J Cereb Blood Flow Metab. 1983;3(1):1–7. doi:10.1038/jcbfm.1983.1.
28. Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood FlowMetab. 1985;5(4):584–590. doi:10.1038/jcbfm.1985.87.
29. Logan J, Fowler JS,Volkow ND, et al. Graphical analysis of reversible radioligand binding from time-activity measurements. J Cereb Blood Flow Metab. 1990;10:740–747.
Intelligenza artificiale e prospettive future
30. Nensa F, Demircioglu A, Rischpler C. Artificial intelligence in nuclear medicine. J Nucl Med. 2019;60(Suppl 2):29S–37S. doi:10.2967/jnumed.118.220590.
31. Aktolun C. Artificial intelligence and radiomics in nuclear medicine: potentials and challenges. Eur J Nucl Med Mol Imaging. 2019;46:2731–2736. doi:10.1007/s00259-019-04593-0.
32. Seifert R, Weber M, Kocakavuk E, Rischpler C, Kersting D. Artificial intelligence and machine learning in nuclear medicine: future perspectives. Semin Nucl Med. 2021;51(2):170–177. doi:10.1053/j.semnuclmed.2020.08.003.
33. Reader AJ, Corda G, Mehranian A, et al. Deep learning for PET image reconstruction. IEEE Trans Radiat Plasma Med Sci. 2021;5:1–25.
34. Visvikis D, Lambin P, Mauridsen KB, et al. Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation. Eur J Nucl Med Mol Imaging. 2022;49:4452–4463. doi:10.1007/s00259-022-05891-w.
35. Saboury B, Bradshaw T, Boellaard R, et al. Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem. J Nucl Med. 2023;64(2):188–196. doi:10.2967/jnumed.121.263703.
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