On Probabilistic Certification of Combined Cancer Therapies Using Strongly Uncertain Models

TL;DR

This paper introduces a probabilistic framework for certifying cancer therapies, ensuring tumor reduction and patient health indicators with high probability despite model uncertainties, aiding treatment planning and resource management.

Contribution

It presents a novel probabilistic certification approach for cancer therapy planning under high uncertainties, specifically applied to combined immunotherapy and chemotherapy.

Findings

Framework guarantees therapy success with high probability.

Applicable to tuning treatment parameters and protocols.

Demonstrated on combined immunotherapy/chemotherapy case.

Abstract

This paper proposes a general framework for probabilistic certification of cancer therapies. The certification is defined in terms of two key issues which are the tumor contraction and the lower admissible bound on the circulating lymphocytes which is viewed as indicator of the patient health. The certification is viewed as the ability to guarantee with a predefined high probability the success of the therapy over a finite horizon despite of the unavoidable high uncertainties affecting the dynamic model that is used to compute the optimal scheduling of drugs injection. The certification paradigm can be viewed as a tool for tuning the treatment parameters and protocols as well as for getting a rational use of limited or expensive drugs. The proposed framework is illustrated using the specific problem of combined immunotherapy/chemotherapy of cancer.