Infinitesimal Perturbation Analysis for Personalized Cancer Therapy Design

TL;DR

This paper models prostate cancer treatment with intermittent androgen suppression using a stochastic hybrid automaton and employs Infinitesimal Perturbation Analysis to optimize therapy thresholds based on patient data.

Contribution

It introduces a novel application of IPA to adaptively optimize PSA thresholds in prostate cancer therapy modeled by SHA.

Findings

Unbiased gradient estimators for therapy thresholds derived using IPA.

Adaptive adjustment of therapy parameters improves treatment outcomes.

Model captures the dynamics of prostate cancer under IAS treatment.

Abstract

We use a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under intermittent androgen suppression (IAS) to study a threshold-based policy for therapy design. IAS is currently one of the most widely used treatments for advanced prostate cancer. Patients undergoing IAS are submitted to cycles of treatment (in the form of androgen deprivation) and off-treatment periods in an alternating manner. One of the main challenges in IAS is to optimally design a therapy scheme, i.e., to determine when to discontinue and recommence androgen suppression. The level of prostate specific antigen (PSA) in a patient's serum is frequently monitored to determine when the patient will be taken off therapy and when therapy will resume. The threshold-based policy we propose is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. Using Infinitesimal Perturbation Analysis (IPA), we derive unbiased gradient estimators of this cost metric with respect to the controllable PSA threshold values based on actual data and show how these estimators can be used to adaptively adjust controllable parameters so as to improve therapy outcomes based on the cost metric defined.