Clinically Relevant Modeling of Tumor Growth and Treatment Response

Yankeelov, Thomas E.; Atuegwu, Nkiruka C.; Hormuth, David A.; Weis, Jared A.; Barnes, Stephanie L.; Miga, Michael I.; Rericha, Erin; Quaranta, Vito

doi:10.1126/scitranslmed.3005686

Cited by 157 publications

(171 citation statements)

References 23 publications

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“…The reliance of the existing modeling literature on parameters that are either extraordinarily difficult or impossible to measure non-invasively fundamentally limits their clinical application. Recasting these models in terms of parameters measured via non-invasive imaging measurements would dramatically improve the clinical relevance of patient-specific tumor growth predictions [1]. …”

Section: Introductionmentioning

confidence: 99%

Predictingin vivoglioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

et al. 2015

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Reaction-diffusion models have been widely used to model glioma growth. However, it has not been shown how accurate this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. Towards this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction-diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor “grown” for ten days as dictated by the reaction-diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n=9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging (DW-MRI), were used to evaluate the model’s accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error < 8.8 %, Dice > 0.92) and local (CCC values > 0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error > 11.7%, Dice < 0.81) and higher local (CCC < 0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction-diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions.

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Section: Introductionmentioning

confidence: 99%

Predictingin vivoglioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

et al. 2015

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“…Examples of the use of imaging data to ascertain the predictivity of tumor growth models can be found in [2, 1, 29, 45, 46, 43, 42, 41]. In this study, we make use of contrast-enhanced MRI to identify the boundaries of the tumor.…”

Section: Observational Data: Magnetic Resonance Imaging In a Murinmentioning

confidence: 99%

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

Lima

Oden

Wohlmuth

et al. 2017

Computer Methods in Applied Mechanics and Engineering

116

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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.

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“…73 developed novel automated tools to capture and quantitate multiplexed imaging features to develop predictive models of the heterogeneity of response of individual cells to perturbations and were able to show heterogeneous cell fates upon exposure to anti-proliferative drugs. In another study, Yankeelov et al, 74 used quantitative tumour imaging methods and developed a model of interactions between cancer cells, stroma and immune cells, vascularisation and the extracellular matrix, that was able to predict treatment response and tumour progression. These modeling approaches have been very useful in enabling us to understand cellular heterogeneity and response to perturbations and lay the foundation of how we can use such information to develop novel methods to treat tumors which are inherently heterogeneous.…”

Section: Cancer Systems Biology Approachesmentioning

confidence: 99%

Cancer Systems Biology: a peek into the future of patient care?

2014

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Traditionally, scientific research has focused on studying individual events, such as single mutations, gene function or the effect of the manipulation of one protein on a biological phenotype. A range of technologies, combined with the ability to develop robust and predictive mathematical models, is beginning to provide information that will enable a holistic view of how the genomic and epigenetic aberrations in cancer cells can alter the homeostasis of signalling networks within these cells, between cancer cells and the local microenvironment, at the organ and organism level. This systems biology process needs to be integrated with an iterative approach wherein hypotheses and predictions that arise from modelling are refined and constrained by experimental evaluation. Systems biology approaches will be vital for developing and implementing effective strategies to deliver personalized cancer therapy. Specifically, these approaches will be important to select those patients most likely to benefit from targeted therapies as well as for the development and implementation of rational combinatorial therapies. Systems biology can help to increase therapy efficacy or bypass the emergence of resistance, thus converting the current (often short term) effects of targeted therapies into durable responses, ultimately to improve quality of life and provide a cure.

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Clinically Relevant Modeling of Tumor Growth and Treatment Response

Cited by 157 publications

References 23 publications

Predictingin vivoglioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

Predictingin vivoglioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

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

Cancer Systems Biology: a peek into the future of patient care?

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