Abstract
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.
Highlights
Speaking, mathematical oncology seeks to develop a collection of in silico models that accurately predict the growth and progression of malignant tumors and their response to treatment [1]
One approach to clinical mathematical oncology, which we call mathematical model-based precision medicine, is to use mathematical models to make patient-specific prognostic and therapeutic predictions which are subsequently employed to direct treatment choices and predict patient outcomes
In our view, such predictions must be conditioned on patient data in order for the strategy to qualify as precision medicine [4,5,6]
Summary
Mathematical oncology seeks to develop a collection of in silico models that accurately predict the growth and progression of malignant tumors and their response to treatment [1]. Image data provides a clear example of this issue, as imaging introduces information loss due to detector noise, low resolution and the possibility of null functions [9] This uncertainty must be accounted for if model-based predictions are to be used reliably in the clinical context. Both maximum likelihood and Bayesian techniques are presented, and both require an accurate likelihood model connecting model parameters to available noisy data.
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