Predicting Patient-Specific Radiotherapy Protocols Based on Mathematical Model Choice for Proliferation Saturation...

Area of Science:

• Oncology
• Medical Physics
• Mathematical Biology

Background:

• Radiotherapy (RT) is a cornerstone of cancer treatment, with over 50% of patients receiving it.
• Advances in RT have historically focused on physical aspects, but incorporating tumor biology is a recent development.
• Tumor response to RT depends on the proportion of proliferating and growth-arrested cells.

Purpose of the Study:

• To investigate the impact of different tumor growth laws on clinical recommendations derived from the proliferation saturation index (PSI) framework.
• To evaluate a generalized logistic equation for modeling tumor kinetics and its influence on PSI-based RT protocols.
• To determine the conditions under which the PSI framework is most reliably applied in clinical practice.

Main Methods:

• Applied a generalized logistic equation to model tumor growth kinetics, encompassing logistic and Gompertzian models.
• Estimated model parameters using clinical data to assess the generalized logistic model's fit across various exponent values.
• Analyzed the dependency of clinical RT recommendations on the assumed underlying tumor growth law within the PSI framework.

Main Results:

• The generalized logistic model effectively described clinical data across a wide range of the generalized logistic exponent.
• Clinical recommendations derived from the PSI framework demonstrated significant dependence on the specific tumor growth law assumed.
• Model parameter estimation showed the generalized logistic model's robustness in fitting diverse growth patterns.

Conclusions:

• The PSI framework's clinical utility is maximized when the underlying tumor growth law is known.
• Alternatively, the PSI framework is most reliable when multiple tumor growth models converge on similar RT fractionation protocols.
• Further research is needed to refine the integration of tumor biology into personalized radiotherapy decision-making.