Towards Personalized Prostate Cancer Therapy Using Delta-Reachability Analysis

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.

HSCC 2015

References in corpus (1)

• Delta-Complete Decision Procedures for Satisfiability over the Reals

Cited by in corpus (1)

• Infinitesimal Perturbation Analysis for Personalized Cancer Therapy Design