Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODE
This work addresses the need for more predictive and personalized models in oncology drug development, though it appears incremental by building on existing neural-ODE methods.
The authors tackled the problem of predicting tumor dynamics and overall survival from longitudinal data by proposing Tumor Dynamic Neural-ODE (TDNODE), which overcomes limitations in handling truncated data and achieves high accuracy in survival prediction.
While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law which possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients' overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.