Structural model selection for pharmacokinetics (PK) and tumor dynamics (TD) is iterative and expert-driven, requiring ODE formulation, nonlinear mixed-effects fitting, and biological plausibility assessment. We present an LLM-agent framework for automated population ODE model discovery, fit locally via SAEM (Monolix).
The workflow iterates: a builder agent proposes candidate ODE systems in...
Drug resistance remains a primary obstacle in oncology, transforming clinical management into a complex, sequential decision-making problem. While Reinforcement Learning (RL) has shown promise in optimizing adaptive dosing for single agents, its application to large polytherapeutic panels—where clinicians choose from numerous drugs with overlapping resistance profiles—remains underexplored....
Glioblastoma remains one of the greatest challenges in oncology, with near-universal recurrence largely driven by diffuse tumor infiltration beyond radiologically visible tumor margins, yet current radiotherapy planning relies on uniform geometric expansions that ignore patient-specific tumor biology and anatomy. Computational growth models and machine learning approaches have the potential to...
Skin cancer is among the most prevalent cancers worldwide, and current monitoring relies on biweekly image-based follow-ups that visually compare lesion changes over time. Although effective, this strategy is reactive and offers limited ability to anticipate future lesion behavior. To address this gap, we propose a predictive framework that leverages routinely collected clinical images to...
Cancer is a complex multiscale disease characterized by heterogeneous cellular behaviors, nonlinear dynamics, and interactions across scales. Computational models provide a formal framework to study cancer progression, predict treatment response, and support translational research. However, significant challenges remain due to spatial and temporal scales, nonlinear behavior, and the...
