Learning-Based sensitivity analysis and feedback design for drug delivery of mixed therapy of cancer in the presence of high model uncertainties

TL;DR

This paper introduces a sensitivity analysis and feedback design methodology for cancer drug delivery therapy, accounting for high patient variability and optimizing drug usage to improve treatment success.

Contribution

It presents a novel approach to identify influential parameters and optimize feedback strategies in cancer therapy under high uncertainties.

Findings

Identifies key parameters affecting therapy success probabilities.

Provides a stochastic optimization framework for safe tumor contraction.

Demonstrates effective visualization of outcomes and drug usage in reduced state space.

Abstract

In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient specific parameters on one hand and to the choice of the parameters that define the drug delivery feedback strategy on the other hand. More precisely, a method is given that enables to extract and rank the most influent parameters that determine the probability of success/failure of a given feedback therapy for a given set of initial conditions over a cloud of realizations of uncertainties. Moreover predictors of the expectations of the amounts of drugs being used can also be derived. This enables to design an efficient stochastic optimization framework that guarantees safe contraction of the tumor while minimizing a weighted sum of the quantities of the different drugs being used. The framework is illustrated and validated using the example of a mixed therapy of cancer involving three combined drugs namely: a chemotherapy drug, an immunology vaccine and an immunotherapy drug. Finally, in this specific case, it is shown that dash-boards can be built in the 2D-space of the most influent state components that summarize the outcomes' probabilities and the associated drug usage as iso-values curves in the reduced state space.