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

TL;DR

This paper reviews the validation of mathematical models used to predict tumor growth and treatment response, emphasizing their role in personalized cancer management and discussing current validation strategies and challenges.

Contribution

It provides a comprehensive overview of modeling frameworks, validation methods, and barriers in applying mathematical models for tumor prediction in clinical settings.

Findings

Validation strategies vary between preclinical and clinical scenarios.

Barriers include data limitations and model complexity.

Potential strategies involve standardized metrics and data sharing.

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.