Optimal drug application on stochastic cancer growth: an approach through path integral control

TL;DR

This paper explores an optimal control framework for drug application in stochastic cancer growth models, utilizing path integral control to determine effective treatment strategies amid uncertainties.

Contribution

It introduces a novel application of path integral control to optimize drug treatment in stochastic tumor growth models with nutrient and drug diffusion.

Findings

Path integral control effectively determines optimal drug dosing strategies.

The model incorporates stochastic disturbances for realistic tumor growth simulation.

Framework provides a basis for personalized cancer treatment planning.

Abstract

We provide an overview of an optimal control problem within a stochastic model of tumor growth, which includes drug application. The model comprises two stochastic differential equations (SDE) representing the diffusion of nutrient and drug concentrations. To account for various uncertainties, stochastic terms are incorporated into the deterministic framework, capturing random disturbances. Control variables, informed by medical principles, are used to regulate drug and nutrient concentrations. In defining the optimal control problem, a stochastic cost function can be established, and a Feynman-type path integral control approach would lead to an optimal drug treatment.