Towards Personalized Prostate Cancer Therapy Using Delta-Reachability Analysis

TL;DR

This paper introduces a computational framework that uses delta-reachability analysis on hybrid automata models to develop personalized hormone therapy schedules for prostate cancer, aiming to delay relapse based on individual patient data.

Contribution

The study presents a novel approach combining hybrid automata modeling with delta-reachability analysis for personalized prostate cancer treatment planning.

Findings

Personalized therapy schedules can be predicted using the proposed method.

The framework effectively models heterogeneous tumor responses.

Results indicate potential for clinical prognostic tools.

Abstract

Recent clinical studies suggest that the efficacy of hormone therapy for prostate cancer depends on the characteristics of individual patients. In this paper, we develop a computational framework for identifying patient-specific androgen ablation therapy schedules for postponing the potential cancer relapse. We model the population dynamics of heterogeneous prostate cancer cells in response to androgen suppression as a nonlinear hybrid automaton. We estimate personalized kinetic parameters to characterize patients and employ $\delta$-reachability analysis to predict patient-specific therapeutic strategies. The results show that our methods are promising and may lead to a prognostic tool for personalized cancer therapy.