Tesi di laurea

Master's Thesis Proposal
Simulation and Benchmarking of Multicellular Tumor Spheroid Growth with Extensions to Model Ionizing Radiation Effects

INTRODUCTION

The modeling of 3D multicellular tumor spheroids (MCTS) is increasingly important as it better represents real cell-cell interactions compared to traditional 2D models. This thesis proposes to replicate and modernize an existing agent-based Monte Carlo model for MCTS growth, initially implemented with FORTRAN [1, 2], using Python and modern programming practices. Following this, the model will be extended to include the effects of ionizing radiation. Collaboration with CNAO and supervision by a medical physicist with coding expertise will be integral to the project.

OBJECTIVES

1.    Replication and Renewal: Recreate the FORTRAN-based model using Python, focusing on modularity, maintainability, and documentation.
2.    Benchmarking: Validate the renewed model by comparing it with published data and replicating the original results.
3.    Model Extension: Extend the model to incorporate the effects of different types of ionizing radiation, including photons, protons, and carbon ions.
4.    Analysis: Evaluate how various radiation types influence tumor growth and assess the robustness of the extended model.

METHODOLOGY

•    Model Replication: Implement the existing model using modern programming practices, ensuring the code is modular and maintainable. This process will involve learning new coding techniques and adapting to contemporary programming standards.
•    Collaboration: Integrate experimental data from CNAO to benchmark and validate the model, working closely with the radiobiological unit.
•    Extension for Radiation Effects: Modify the model to simulate the impact of photon, proton, and carbon ion irradiation on tumor growth.
•    Simulation and Analysis: Conduct simulations to assess the effects of different radiation types and perform sensitivity analyses to refine the model.

LEARNING CURVE

Building the code from scratch will involve a significant learning curve for both the student and the supervisor. The student will gain experience in modern coding practices and model development, while the supervisor will provide guidance to navigate this learning process. Emphasis will be placed on best practices in coding, including modular design and thorough documentation, to ensure a robust and maintainable model.

TIMELINE (APPROX. 1 YEAR)

Literature review - 1 month
Model replication and renewal - 3-5 months
Benchmarking and validation - 2 months
Model extension for radiation effects (*) - 3 months
Simulation and data analysis (*) - 1-2 months
(* )additional goals that will be pursued if time permits.

CONCLUSION

This thesis will modernize an important 3D tumor growth model and extend it to include radiation effects, providing new insights into cancer treatment optimization. The project will involve close collaboration with CNAO and supervision from a Medical Physicist with expertise in Python, offering valuable learning opportunities for the student in coding and model development.

REQUIREMENTS

Knowledge of a programming language is required (knowledge of Python is a plus).

REFERENCES

1.    Ruiz-Arrebola, S. "An on-lattice agent-based Monte Carlo model simulating the growth kinetics of multicellular tumor spheroids." Journal of Computational Physics, 2017. DOI: 10.1016/j.jcp.2017.07.036.
2.    Ruiz-Arrebola, S., Álvarez-Sánchez, J., & García-Sánchez, F. "Evaluation of Classical Mathematical Models of Tumor Growth Using an On-Lattice Agent-Based Monte Carlo Model." Journal of Computational Physics, 2020. DOI: 10.1016/j.jcp.2020.109035.

CONTACTS: matteo.bagnalasta@cnao.it giuseppe.magro@cnao.it fisicimedici@cnao.it