TumorGrowthToolkit

Welcome to TumorGrowthToolkit, a Python package dedicated to the simulation and analysis of tumor growth using numerical solvers for Partial Differential Equations.

Models

• Reaction-diffusion multi-cell with a nutrient field (1: proliferative/diffusive, 2: necrotic) (FK_2c)
• Reaction-diffusion single cell (FK)
• Diffusion Tensor Imaging (DTI) based Fisher-Kolmogorov model (FK_DTI)

Installation

To use the solver, first install the package by cloning this repository and using:

pip install -e .

Running solvers/plotting

Reaction-diffusion single cell (Fisher-Kolmogorov)

• FK_example.ipynb (plain)
• FK_exampleAtlas.ipynb (in example brain tissue)

Reaction-diffusion multi-cell with a nutrient field (1: proliferative/diffusive, 2: necrotic) (FK_2c)

• FK_2c_example.ipynb

Diffusion Tensor Imaging (DTI) based Fisher-Kolmogorov model (FK_DTI)

Here, the tumor diffusion is based on measured diffusion data (DTI) instead of the white and gray matter tissue segmentation.

• FK_DTI_example.ipynb

Synthetic patients generator

Example usage:

cd synthetic_gens
python run_gen_FK_2c.py

Creates synthetic patients with PET images and tumor segmentations (enhancing core, necrotic core, edema).

Wishlist

• inherit more from the base FK class. Especially the time evolution and stopping parameters.

References

In publications using TumorGrowthToolkit pleace cite:

1. Balcerak, M., Ezhov, I., Karnakov, P., Litvinov, S., Koumoutsakos, P., Weidner, J., Zhang, R. Z., Lowengrub, J. S., Wiestler, B., & Menze, B. (2023). Individualizing Glioma Radiotherapy Planning by Optimization of a Data and Physics Informed Discrete Loss. arXiv preprint arXiv:2312.05063.