Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs
This work addresses the challenge of sparse data in oncology modeling for researchers, but it is incremental as it adapts an existing PINN framework to a specific domain.
The authors tackled the problem of modeling adoptive cell therapy in bladder cancer with sparse biological data by extending physics-informed neural networks (PINNs) to incorporate biological constraints, resulting in a method that generalizes well with few training examples and demonstrates strong convergence using metrics like MSE, MAE, and MAPE.
Physics-informed neural networks (PINNs) are neural networks that embed the laws of dynamical systems modeled by differential equations into their loss function as constraints. In this work, we present a PINN framework applied to oncology. Here, we seek to learn time-varying interactions due to a combination therapy in a tumor microenvironment. In oncology, experimental data are often sparse and composed of a few time points of tumor volume. By embedding inductive biases derived from prior information about a dynamical system, we extend the physics-informed neural networks (PINN) and incorporate observed biological constraints as regularization agents. The modified PINN algorithm is able to steer itself to a reasonable solution and can generalize well with only a few training examples. We demonstrate the merit of our approach by learning the dynamics of treatment applied intermittently in an ordinary differential equation (ODE) model of a combination therapy. The algorithm yields a solution to the ODE and time-varying forms of some of the ODE model parameters. We demonstrate a strong convergence using metrics such as the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).