Data-Driven Parameter Identification for Tumor Growth Models

Liu Liu; Qinyu Xu; Xiaoqian Xu; Yifei Wang

arxiv: 2511.15940 · v2 · pith:3W3WFNV2new · submitted 2025-11-20 · 🧮 math.AP

Data-Driven Parameter Identification for Tumor Growth Models

Liu Liu , Yifei Wang , Qinyu Xu , Xiaoqian Xu This is my paper

classification 🧮 math.AP

keywords growthtumordatadata-drivenmodelingaccuratelyaddressadopt

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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) and DeepONet, 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.

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Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks
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Bayesian PINN integrates Gompertz dynamics and HMC sampling to predict tumor growth from sparse CT data, achieving log-space RMSE of 0.20 with well-calibrated 95% credible intervals on 30 NLST patients.