Simulating Cancer Recurrence Patterns From Post-Treatment Viable Tumor Burden Distributions

Area of Science:

• Mathematical Oncology
• Tumor Growth Dynamics
• Clinical Trial Modeling

Background:

• Ordinary differential equation models accurately describe tumor growth and treatment response.
• Extending continuous models to simulate population-level outcomes is crucial for understanding recurrence patterns.

Purpose of the Study:

• To develop a novel mathematical model for simulating tumor regrowth and population-level recurrence patterns.
• To conceptualize post-treatment viable tumor burden distributions within a treatment population.
• To connect tumor dynamics models with recurrence patterns for improved clinical trial interpretation.

Main Methods:

• Utilized a mathematical model of tumor regrowth dynamics incorporating a minimum viable tumor burden threshold for cure.
• Simulated tumor regrowth until the tumor burden exceeded a detection threshold to model Kaplan-Meier curves.
• Qualitatively fitted the model to real-world recurrence data from a head and neck cancer clinical trial (RTOG 9003).

Main Results:

• Explored the impact of model parameters and growth laws on simulated Kaplan-Meier curve shapes.
• Demonstrated the model's utility in understanding clinical trial results.
• Presented qualitative fitting of the model to clinical recurrence data.

Conclusions:

• The theoretical framework connects tumor dynamics models to recurrence patterns.
• Provides a new methodology for interpreting Kaplan-Meier curves.
• Offers insights into clinical trial failures and guidance for redesigning future trials for success.