Archives of Computational Methods in Engineering

, Volume 23, Issue 4, pp 735–779 | Cite as

Toward Predictive Multiscale Modeling of Vascular Tumor Growth

Computational and Experimental Oncology for Tumor Prediction
  • J. Tinsley Oden
  • Ernesto A. B. F. Lima
  • Regina C. Almeida
  • Yusheng Feng
  • Marissa Nichole Rylander
  • David Fuentes
  • Danial Faghihi
  • Mohammad M. Rahman
  • Matthew DeWitt
  • Manasa Gadde
  • J. Cliff Zhou
Original Paper

Abstract

New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patient-specific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the presence of uncertainties fundamental to predictive medical science, are described which are based on the notion of Bayesian model plausibilities. Also, as part of this general approach, new codes for determining the sensitivity of model outputs to variations in model parameters are described that provide a basis for assessing the importance of model parameters and controlling and reducing the number of relevant model parameters. Model specific data is to be accessible through careful and model-specific platforms in the Tumor Engineering Laboratory. We describe parallel computer platforms on which large-scale calculations are run as well as specific time-marching algorithms needed to treat stiff systems encountered in some phase-field mixture models. We also cover new non-invasive imaging and data classification methods that provide in vivo data for model validation. The study concludes with a brief discussion of future work and open challenges.

Keywords

Tumor growth models Bayesian calibration and validation 3D tumor platforms Image processing 

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Copyright information

© CIMNE, Barcelona, Spain 2015

Authors and Affiliations

  • J. Tinsley Oden
    • 1
  • Ernesto A. B. F. Lima
    • 1
  • Regina C. Almeida
    • 1
    • 2
  • Yusheng Feng
    • 4
  • Marissa Nichole Rylander
    • 1
    • 3
    • 7
  • David Fuentes
    • 5
  • Danial Faghihi
    • 1
  • Mohammad M. Rahman
    • 4
  • Matthew DeWitt
    • 6
  • Manasa Gadde
    • 7
  • J. Cliff Zhou
    • 1
  1. 1.Institute for Computational Engineering and Sciences (ICES)The University of Texas at AustinAustinUSA
  2. 2.National Laboratory for Scientific Computing (LNCC)PetrópolisBrazil
  3. 3.Department of Mechanical EngineeringThe University of Texas at AustinAustinUSA
  4. 4.Center for Simulation, Visualization and Real-Time Prediction (SiViRT), One UTSA Circle, AET 2.332The University of Texas at San AntonioSan AntonioUSA
  5. 5.Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  6. 6.Virginia Tech - Wake Forest School of Biomedical Engineering and SciencesVirginia TechBlacksburgUSA
  7. 7.Department of Biomedical EngineeringThe University of Texas at AustinAustinUSA

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