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

• Published in: Computer methods in applied mechanics and engineering Vol. 327; no. C; pp. 277 - 305
• Main Authors: Lima, E.A.B.F., Oden, J.T., Wohlmuth, B., Shahmoradi, A., Hormuth, D.A., Yankeelov, T.E., Scarabosio, L., Horger, T.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2017; Elsevier BV; Elsevier
• Subjects: Bayesian analysis; Bayesian inference; Calibration and Validation of Phenomenological models; Cancer; Computer simulation; Deformation effects; Finite element method; Markov chains; Mathematical models; Model plausibilities; Monte Carlo methods; Monte Carlo simulation; MRI imaging; Nonlinear differential equations; Nonlinear equations; Numerical analysis; Parameter uncertainty; Partial differential equations; Prediction models; Radiation effects; Radiation therapy; Studies; Tumors; Vascular tissue; Model plausibilities; Monte Carlo methods; Calibration and Validation of Phenomenological models; MRI imaging; Bayesian inference; Cancer; statistical sampling; phenomenological models; radiotherapy; model plausibilities; calibration; validation

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,...