Data-Driven Parameter Identification for Tumor Growth Models

TL;DR

This paper demonstrates how Physics-Informed Neural Networks can effectively estimate parameters in nonlinear tumor growth models using limited and noisy real-world data, advancing data-driven cancer modeling.

Contribution

It introduces a novel application of PINNs for parameter identification in tumor growth models, highlighting their advantages with scarce and noisy data.

Findings

PINNs successfully estimated tumor growth parameters from real data.

Deep learning enhances biological modeling accuracy.

The approach is robust to data scarcity and noise.

Abstract

Modeling tumor growth accurately is essential for understanding cancer progression and informing treatment strategies. To estimate the parameters in the tumor growth model described by a nonlinear PDE, we adopt Physics-Informed Neural Networks (PINNs), which show advantages especially when the observation data is scarce and contains noise. With the help of real-life lab data, we have demonstrated the potential of applying deep learning tools to address data-driven modeling for tumor growth in biology.