Adaptive tumor growth forecasting via neural & universal ODEs
This work addresses the challenge of personalized tumor growth prediction for clinicians, though it appears incremental as it builds on existing SciML methods.
The study tackled the problem of forecasting tumor growth by adapting classical models to patient-specific variability using Neural ODEs and UDEs, resulting in improved predictive accuracy for clinical treatment strategies.
Forecasting tumor growth is critical for optimizing treatment. Classical growth models such as the Gompertz and Bertalanffy equations capture general tumor dynamics but may fail to adapt to patient-specific variability, particularly with limited data available. In this study, we leverage Neural Ordinary Differential Equations (Neural ODEs) and Universal Differential Equations (UDEs), two pillars of Scientific Machine Learning (SciML), to construct adaptive tumor growth models capable of learning from experimental data. Using the Gompertz model as a baseline, we replace rigid terms with adaptive neural networks to capture hidden dynamics through robust modeling in the Julia programming language. We use our models to perform forecasting under data constraints and symbolic recovery to transform the learned dynamics into explicit mathematical expressions. Our approach has the potential to improve predictive accuracy, guiding dynamic and effective treatment strategies for improved clinical outcomes.