Selection, calibration, and validation of models of tumor growth

E A B F Lima; J T Oden; D A Hormuth 2nd; T E Yankeelov; R C Almeida

doi:10.1142/S021820251650055X

Selection, calibration, and validation of models of tumor growth

Math Models Methods Appl Sci. 2016 Nov;26(12):2341-2368. doi: 10.1142/S021820251650055X. Epub 2016 Oct 3.

Authors

E A B F Lima¹, J T Oden¹, D A Hormuth 2nd¹, T E Yankeelov^{1

2

3}, R C Almeida⁴

Affiliations

¹ Center of Computational Oncology, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX 78712-1229, USA.
² Department of Internal Medicine, Livestrong Cancer Institutes, Dell Medical School, Health Learning Building, The University of Texas at Austin, 1501 Red River Street, Austin, TX 78712, USA.
³ Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton, Austin, TX 78712, USA.
⁴ National Laboratory for Scientific Computing (LNCC), Av. Getúlio Vargas, 333, Quitandinha, Petrópolis, Rio de Janeiro 25651-075, Brazil.

Abstract

This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology (in vivo). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL.

Keywords: 22E46; 53C35; 57S20; Predictive model; cancer; computational oncology; model calibration; systems biology.

Abstract

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