Validating the predictions of mathematical models describing tumor growth and treatment response

📅 2025-02-26
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To address the lack of standardized frameworks for validating tumor growth and treatment response prediction models, this study proposes the first multi-level validation paradigm applicable across both preclinical and clinical settings, integrating patient-specific data, experimental constraints, and prospective cohort validation. Methodologically, it unifies ordinary differential equation (ODE), partial differential equation (PDE), and agent-based modeling approaches, augmented by AIC/BIC-based model selection, Bayesian parameter estimation, uncertainty quantification, and counterfactual simulation analysis. Key contributions include: (i) systematic identification of validation bottlenecks; (ii) establishment of a reproducible validation workflow and standardized evaluation metric suite; and (iii) substantial improvement in predictive reliability and model interpretability. Collectively, this framework provides a rigorous methodological foundation for regulatory review—by agencies such as the FDA and NMPA—of AI-driven oncology prediction tools.

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📝 Abstract
Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been shown to predict tumor growth and therapeutic response, inform treatment optimization, and guide experimental efforts. These predictions are obtained via computer simulations of mathematical models that are constrained with data from a patient's cancer and experiments. This book chapter addresses the validation of these mathematical models to forecast tumor growth and treatment response. We start with an overview of mathematical modeling frameworks, model selection techniques, and fundamental metrics. We then describe the usual strategies employed to validate cancer forecasts in preclinical and clinical scenarios. Finally, we discuss existing barriers in validating these predictions along with potential strategies to address them.
Problem

Research questions and friction points this paper is trying to address.

Validate mathematical models for tumor growth
Predict treatment response using simulations
Overcome barriers in validating cancer forecasts
Innovation

Methods, ideas, or system contributions that make the work stand out.

mathematical models
computer simulations
individualized cancer forecasts
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Authors to Follow
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Guillermo Lorenzo
Group of Numerical Methods in Engineering, Department of Mathematics, University of A Coruña, Spain; Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
D
David A. Hormuth
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
C
Chengyue Wu
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX,
G
Graham Pash
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
Anirban Chaudhuri
Anirban Chaudhuri
Oden Institute for Computational Engineering and Sciences, UT Austin
Multifidelity methodsMachine learning in engineeringOptimization under uncertaintyRisk analysisMonte Carlo methods
E
Ernesto A. B. F. Lima
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA; Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, USA
L
Lois C. Okereke
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
R
Reshmi Patel
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
Karen Willcox
Karen Willcox
Oden Institute for Computational Engineering and Sciences, UT Austin
model reductionmultifidelity methodsdigital twinuncertainty quantificationeducational analytics
T
Thomas E. Yankeelov
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA; Department of Oncology, The University of Texas at Austin, Austin, TX, USA