Math Biosci Eng. 2020 Sep 25;17(6):6531-6556. doi: 10.3934/mbe.2020341.
Mathematical biosciences and engineering : MBE
Nick Henscheid
PMID: 33378865 PMCID: PMC7780222 DOI: 10.3934/mbe.2020341
The use of mathematical tumor growth models coupled to noisy imaging data has been suggested as a possible component in the push towards precision medicine. We discuss the generation of population and patient-specific virtual populations in this context, providing in silico experiments to demonstrate how intra- and inter-patient heterogeneity can be estimated by applying rigorous statistical procedures to noisy molecular imaging data, and how the noise properties of such data can be analyzed to estimate uncertainties in predicted patient outcomes.
Keywords: emission computed tomography ; mathematical onoclogy ; molecular imaging ; precision medicine ; virtual clinical trials ; virtual populations