Stratified breast cancer follow-up using a partially observable MDP

J.W.M. Otten, Annemieke Witteveen, Ingrid Vliegen, Sabine Siesling, Judith B. Timmer, Maarten J. IJzerman

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

    6 Citations (Scopus)


    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
    EditorsRichardus J. Boucherie, Nico van Dijk
    Place of PublicationCham
    Number of pages22
    ISBN (Print)978-3-319-47764-0
    Publication statusPublished - 1 Mar 2017

    Publication series

    NameInternational Series in Operations Research & Management Science
    PublisherSpringer International Publishing


    • EWI-27869, Partially observable markov decision process, Breast cancer, Optimal policies, IR-104507, Stratified follow-up


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