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, Maarten IJzerman

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

6 Citations (Scopus)
28 Downloads (Pure)

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 horzion 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.
Original languageEnglish
Title of host publicationMarkov Decision Processes in Practice
EditorsRichard J. Boucherie, Nico M. van Dijk
Place of PublicationCham
PublisherSpringer
Pages223-244
Number of pages22
ISBN (Electronic)978-3-319-47766-4
ISBN (Print)978-3-319-47764-0
DOIs
Publication statusPublished - Mar 2017

Publication series

NameInternational Series in Operations Research & Management Science
PublisherSpringer International Publishing
Volume248
ISSN (Print)0884-8289

Keywords

  • Partially observable Markov decision process (POMDP)
  • Breast cancer
  • Optimal policies
  • Stratified follow-up
  • Markov decision process (MDP)
  • 2023 OA procedure

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