How Mathematical Forms of Chemotherapy and Radiotherapy Bias Model-Optimized Predictions: Implications for Model Selection

TL;DR

This paper examines how different mathematical models for chemotherapy and radiotherapy influence optimized treatment protocols, highlighting the importance of bias analysis for reliable personalized cancer therapy predictions.

Contribution

It systematically compares multiple chemotherapy and radiotherapy models to reveal how model assumptions bias treatment optimization and emphasizes comprehensive uncertainty quantification in model selection.

Findings

Model assumptions significantly alter optimal treatment protocols.

Different models produce contradictory treatment recommendations.

Bias and sensitivity analysis are crucial for robust personalized therapy predictions.

Abstract

The move towards personalized treatment and digital twins for cancer therapy requires a complete understanding of the mathematical models upon which these optimized simulation-based strategies are formulated. This study investigates the influence of mathematical model selection on the optimization of chemotherapy and radiotherapy protocols. By examining three chemotherapy models (log-kill, Norton-Simon, and Emax), and three radiotherapy models (linear-quadratic, proliferation saturation index, and continuous death-rate), we identify similarities and significant differences in the optimized protocols. We demonstrate how the assumptions built into the model formulations heavily influence optimal treatment dosing and sequencing, potentially leading to contradictory results. Further, we demonstrate how different model forms influence predictions in the adaptive therapy setting. As treatment decisions increasingly rely on simulation-based strategies, unexamined model assumptions can introduce bias, leading to model-dependent recommendations that may not be generalizable. This study highlights the importance of basing model selection on a full analysis of bias, sensitivity, practical parameter identifiability and/or inferred parameter posteriors, as a part of the uncertainty quantification process, rather than solely relying on information criterion. Understanding how model choice impacts predictions guiding personalized treatment planning with sufficient uncertainty quantification analysis, will lead to more robust and generalizable predictions.