Selection and validation of predictive models of radiation effects on tumor growth based on noninvasive imaging data

Abstract

The use of mathematical and computational models for reliable predictions of tumor growth and decline in living organisms is one of the foremost challenges in modern predictive science, as it must cope with uncertainties in observational data, model selection, model parameters, and model inadequacy, all for very complex physical and biological systems. In this paper, large classes of parametric models of tumor growth in vascular tissue are discussed including models for radiation therapy. Observational data is obtained from MRI of a murine model of glioma and observed over a period of about three weeks, with X-ray radiation administered 14.5 days into the experimental program. Parametric models of tumor proliferation and decline are presented based on the balance laws of continuum mixture theory, particularly mass balance, and from accepted biological hypotheses on tumor growth. Among these are new model classes that include characterizations of effects of radiation and simple models of mechanical deformation of tumors. The Occam Plausibility Algorithm (OPAL) is implemented to provide a Bayesian statistical calibration of the model classes, 39 models in all, as well as the determination of the most plausible models in these classes relative to the observational data, and to assess model inadequacy through statistical validation processes. Discussions of the numerical analysis of finite element approximations of the system of stochastic, nonlinear partial differential equations characterizing the model classes, as well as the sampling algorithms for Monte Carlo and Markov chain Monte Carlo (MCMC) methods employed in solving the forward stochastic problem, and in computing posterior distributions of parameters and model plausibilities are provided. The results of the analyses described suggest that the general framework developed can provide a useful approach for predicting tumor growth and the effects of radiation.

Computer simulations suggest that prostate enlargement due to benign prostatic hyperplasia mechanically impedes prostate cancer growth

“…These models aim at complementing the current clinical practice in oncology by assisting physicians in better estimating disease progression and designing optimal treatment schemes. In particular, several mathematical models have incorporated the tumor mass effect to improve the prediction of the growth of various types of cancers (32)(33)(34)(35)(36)(37)(38). Fig.…”

Local and nonlocal phase-field models of tumor growth and invasion due to ECM degradation

“…This study complements and extends recent work on general phase-field models reported in [22,40,41]. The models developed and analyzed there are intended to depict phenomena at the mesoscale and macroscale where tumor constituents are determined by fields representing volume fractions of mass concentrations of various species.…”

“…We will ignore thermal effects, and also, for the moment, mechanical deformations, see e.g. [40], as well as convective flow velocities in the material time derivatives, see e.g. [22], concentrating on mass conservation.…”

PDE-constrained optimization in medical image analysis

“…There has only been little work on model-based image analysis in brain tumor imaging based on PDE-constrained optimization [47,70,99,110,111,125,127,135,165]; 12 the works in [47,70,99,110,111,125,165] consider adjoint based approaches for numerical optimization. Others use derivative-free optimization [40,110,114,127,133,139,206,207], finite-difference approximations to the gradient [101], or tackle the parameter estimation problem within a Bayesian framework [48,91,116,117,122,123,138,154] (see Rem. 1).…”