Mesoscopic Dynamics and Its Hierarchical Probabilistic Formulation for Cancer Therapy

Current approaches to cancer therapy often relate therapeutic input directly to outcomes, leaving the intermediate dynamics largely unrepresented. Recent work formulated cancer therapy as a continuous process in which an initial state-space organization and therapeutic input give rise to therapeutic trajectory evolution, attractor formation, and an observable outcome. The present work defines mesoscopic dynamics between therapeutic input and outcome. It then applies the probability chain rule to factorize the joint conditional distribution of possible therapeutic trajectories, attractors, and outcomes. The resulting hierarchical probabilistic formulation provides a probabilistic mathematical representation of mesoscopic therapeutic dynamics by making explicit the conditional organization of possible trajectories, attractors, and outcomes. Together, mesoscopic dynamics and its hierarchical probabilistic formulation provide a conceptual framework for connecting therapeutic input to clinical outcome.