Validating the predictions of mathematical models describing tumor growth and treatment response
Journal:
arXiv
Published Date:
Feb 26, 2025
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.
