Stratified Breast Cancer Follow-Up Using a Partially Observable MDP

J. W. M. Otten, A. Witteveen (), I. M. H. Vliegen, S. Siesling, J. B. Timmer and M. J. IJzerman
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J. W. M. Otten: University of Twente
A. Witteveen: University of Twente
I. M. H. Vliegen: University of Twente
S. Siesling: University of Twente
J. B. Timmer: University of Twente
M. J. IJzerman: University of Twente

Chapter Chapter 7 in Markov Decision Processes in Practice, 2017, pp 223-244 from Springer

Abstract: Abstract Frequency and duration of follow-up for patients with breast cancer is still under discussion. Current follow-up consists of annual mammography for the first five years after treatment and does not depend on the personal risk of developing a locoregional recurrence (LRR) or second primary tumor. Aim of this study is to gain insight in how to allocate resources for optimal and personal follow-up. We formulate a discrete-time Partially Observable Markov Decision Process (POMDP) with a finite horizon in which we aim to maximize the total expected number of quality-adjusted life years (QALYs). Transition probabilities were obtained from data from the Netherlands Cancer Registry (NCR). Twice a year the decision is made whether or not a mammography will be performed. Recurrent disease can be detected by both mammography or women themselves (self-detection). The optimal policies were determined for three risk categories based on differentiation of the primary tumor. Our results suggest a slightly more intensive follow-up for patients with a high risk and poorly differentiated tumor, and a less intensive schedule for the other risk groups.

Keywords: Optimal Policy; Markov Decision Process; Belief State; Optimality Equation; Partially Observable Markov Decision Process (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-47766-4_7

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