EVALUATION OF TUMORGROWTHTOOLKIT: A PYTHON PACKAGE FOR SIMULATION AND ANALYSIS OF TUMOR GROWTH USING NUMERICAL SOLVERS FOR PARTIAL DIFFERENTIAL EQUATION

This thesis evaluates the TumorGrowthToolkit, a comprehensive software suite for modeling and simulating tumor growth. The toolkit integrates diverse mathematical models and computational algorithms to simulate various tumor growth scenarios. From the evaluation,each model offers unique advantages in capturing different aspects of tumor growth and treatment response. The logistic and Gompertzian models provide robust general predictions,while reaction-diffusion and ABMs offer detailed insights into spatial dynamics and cellularinteractions.The hybrid models effectively bridge these approaches, offering comprehensive simulations. The evaluation reveals that the TumorGrowthToolkit effectively reproduces key tumor growth patterns observed in experimental studies, demonstrating high accuracy in modeling tumor expansion and response to treatment. Specifically, simulations of solid tumor growth and metastatic spread show a close match with clinical data, indicating the toolkit's potential for predicting tumor behavior under different therapeutic regimes. Performance analysis highlights the toolkit's computational efficiency and scalability, making it suitable for both small-scale studies and large-scale simulations. The results underscore theTumorGrowthToolkit's value as a research tool for understanding tumor dynamics and optimizing treatment strategies.Recommendations for future development focus on refining user interaction and expanding model capabilities to better address the complexities of cancer biology.