Speaker
Description
Once diagnosed, cancer requires a fast, inexpensive and reliable assessment of the current state and potential progression of the disease. A new method for estimating tumor cell diffusivity $D$ and proliferation rate $\gamma$ from single-point-in-time routine biopsies aims to deliver just that, and the ratio of its estimates $D/\gamma$ is a promising candidate for a new biomarker for risk-stratification in radiotherapy. Here, we extend the findings of the researchers at MD Anderson Cancer Center \cite{Pasetto.2024}, who developed the method, by providing a simulation-based validation. The method is applied to \textit{in silico} biopsies which are generated by solving the three-dimensional reaction-diffusion (RD) equation for different growth terms (exponential and logistic) with a Dirac-Delta initial condition, and transforming the continuous results into spatial point patterns via a form of reverse coarse-graining.
First results of this validation process have been presented at SMB 2025, which were performed using a less realistic two-dimensional RD equation and an inferior reverse coarse-graining algorithm.
Bibliography
@article{Pasetto.2024,
author = {Pasetto, Stefano and Montejo, Michael and Zahid, Mohammad U. and Rosa, Marilin and Gatenby, Robert and Schlicke, Pirmin and Diaz, Roberto and Enderling, Heiko},
year = {2024},
title = {Calibrating tumor growth and invasion parameters with spectral spatial analysis of cancer biopsy tissues},
pages = {1--9},
volume = {10},
number = {1},
issn = {2056-7189},
journal = {npj Systems Biology and Applications},
doi = {10.1038/s41540-024-00439-0},
file = {Pasetto, Montejo et al. 2024 - Calibrating tumor growth and invasion:Attachments/Pasetto, Montejo et al. 2024 - Calibrating tumor growth and invasion.pdf:application/pdf}
}
