Simulating tumor volume dynamics in response to radiotherapy: Implications of model selection

Area of Science:

• Mathematical Oncology
• Radiobiology Modeling
• Cancer Treatment Dynamics

Background:

• Mathematical modeling is crucial for understanding radiobiology and optimizing radiotherapy (RT) treatment schedules.
• Previous models have incorporated varying levels of biological complexity to simulate RT response.

Purpose of the Study:

• To compare three distinct mathematical models of tumor volume dynamics under radiotherapy.
• To investigate the implications of model selection on predicting tumor response and parameterization.
• To introduce and analyze a new metric, the point of maximum reduction of tumor volume (MRV).

Main Methods:

• Compared exponential and logistic growth models with different RT effects (direct volume reduction vs. carrying capacity reduction).
• Analyzed tumor volume change rates, performed parameter sensitivity and identifiability analyses.
• Investigated the impact of parameter sensitivity on tumor volume trajectories and MRV timing.

Main Results:

• Distinct differences in the timing of the maximum reduction of tumor volume (MRV) were observed based on model selection.
• Parameter identifiability and sensitivity analyses revealed parameter interdependence, requiring high tumor growth rates for independent identification.
• Model selection artifacts can influence the predicted tumor volume dynamics and RT impact timing.

Conclusions:

• The choice of mathematical model significantly affects the understanding and prediction of tumor response to radiotherapy.
• Caution is necessary when selecting models for RT response due to potential artifacts.
• Generated falsifiable hypotheses regarding MRV timing that can be tested with high-frequency longitudinal data.