IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v196y2012i1p683-70510.1007-s10479-010-0741-y.html
   My bibliography  Save this article

Scheduling the adjuvant endocrine therapy for early stage breast cancer

Author

Listed:
  • Sera Kahruman
  • Elif Ulusal
  • Sergiy Butenko
  • Illya Hicks
  • Kathleen Diehl

Abstract

Based on the data available through published trial results, we build a mixed integer nonlinear programming (MINLP) model in order to find an optimal treatment plan for a given HR+ early stage breast cancer patient who is postmenopausal. The objective is to maximize the disease-free survival percentage at the end of the treatment period subject to the constraints on the risk of contralateral breast cancer and the risks of several side effects, including endometrial cancer, thromboembolic events, cardiovascular diseases, bone fractures, hot flushes, and vaginal bleeding. The results of numerical experiments suggest the effectiveness of some of the schedules currently used in practice, as well as suggest some attractive alternative treatment plans. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Sera Kahruman & Elif Ulusal & Sergiy Butenko & Illya Hicks & Kathleen Diehl, 2012. "Scheduling the adjuvant endocrine therapy for early stage breast cancer," Annals of Operations Research, Springer, vol. 196(1), pages 683-705, July.
    • Handle: RePEc:spr:annopr:v:196:y:2012:i:1:p:683-705:10.1007/s10479-010-0741-y
    DOI: 10.1007/s10479-010-0741-y
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-010-0741-y
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-010-0741-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. H. Edwin Romeijn & Ravindra K. Ahuja & James F. Dempsey & Arvind Kumar, 2006. "A New Linear Programming Approach to Radiation Therapy Treatment Planning Problems," Operations Research, INFORMS, vol. 54(2), pages 201-216, April.
    2. Zvia Agur & Refael Hassin & Sigal Levy, 2006. "Optimizing Chemotherapy Scheduling Using Local Search Heuristics," Operations Research, INFORMS, vol. 54(5), pages 829-846, October.
    3. Eva Lee & Tim Fox & Ian Crocker, 2003. "Integer Programming Applied to Intensity-Modulated Radiation Therapy Treatment Planning," Annals of Operations Research, Springer, vol. 119(1), pages 165-181, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nazila Bazrafshan & M. M. Lotfi, 2020. "A finite-horizon Markov decision process model for cancer chemotherapy treatment planning: an application to sequential treatment decision making in clinical trials," Annals of Operations Research, Springer, vol. 295(1), pages 483-502, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias Ehrgott & Çiğdem Güler & Horst Hamacher & Lizhen Shao, 2010. "Mathematical optimization in intensity modulated radiation therapy," Annals of Operations Research, Springer, vol. 175(1), pages 309-365, March.
    2. Timothy C. Y. Chan & Tim Craig & Taewoo Lee & Michael B. Sharpe, 2014. "Generalized Inverse Multiobjective Optimization with Application to Cancer Therapy," Operations Research, INFORMS, vol. 62(3), pages 680-695, June.
    3. Marc C. Robini & Feng Yang & Yuemin Zhu, 2020. "A stochastic approach to full inverse treatment planning for charged-particle therapy," Journal of Global Optimization, Springer, vol. 77(4), pages 853-893, August.
    4. Shabbir Ahmed & Ozan Gozbasi & Martin Savelsbergh & Ian Crocker & Tim Fox & Eduard Schreibmann, 2010. "An Automated Intensity-Modulated Radiation Therapy Planning System," INFORMS Journal on Computing, INFORMS, vol. 22(4), pages 568-583, November.
    5. Dursun, Pınar & Taşkın, Z. Caner & Altınel, İ. Kuban, 2019. "The determination of optimal treatment plans for Volumetric Modulated Arc Therapy (VMAT)," European Journal of Operational Research, Elsevier, vol. 272(1), pages 372-388.
    6. Dunbar, Michelle & O’Brien, Ricky & Froyland, Gary, 2020. "Optimising lung imaging for cancer radiation therapy," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1038-1052.
    7. Özge Karanfil & Yaman Barlas, 2008. "A Dynamic Simulator for the Management of Disorders of the Body Water Homeostasis," Operations Research, INFORMS, vol. 56(6), pages 1474-1492, December.
    8. Z. Caner Taşkın & J. Cole Smith & H. Edwin Romeijn & James F. Dempsey, 2010. "Optimal Multileaf Collimator Leaf Sequencing in IMRT Treatment Planning," Operations Research, INFORMS, vol. 58(3), pages 674-690, June.
    9. Thomas Bortfeld & Timothy C. Y. Chan & Alexei Trofimov & John N. Tsitsiklis, 2008. "Robust Management of Motion Uncertainty in Intensity-Modulated Radiation Therapy," Operations Research, INFORMS, vol. 56(6), pages 1461-1473, December.
    10. Z. Taşkın & J. Smith & H. Romeijn, 2012. "Mixed-integer programming techniques for decomposing IMRT fluence maps using rectangular apertures," Annals of Operations Research, Springer, vol. 196(1), pages 799-818, July.
    11. Natalie Kronik & Yuri Kogan & Moran Elishmereni & Karin Halevi-Tobias & Stanimir Vuk-Pavlović & Zvia Agur, 2010. "Predicting Outcomes of Prostate Cancer Immunotherapy by Personalized Mathematical Models," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-8, December.
    12. Mustafa Sir & Marina Epelman & Stephen Pollock, 2012. "Stochastic programming for off-line adaptive radiotherapy," Annals of Operations Research, Springer, vol. 196(1), pages 767-797, July.
    13. Wei Chen & Yixin Lu & Liangfei Qiu & Subodha Kumar, 2021. "Designing Personalized Treatment Plans for Breast Cancer," Information Systems Research, INFORMS, vol. 32(3), pages 932-949, September.
    14. Xiuxian Wang & Na Geng & Jianxin Qiu & Zhibin Jiang & Liping Zhou, 2020. "Markov model and meta-heuristics combined method for cost-effectiveness analysis," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 213-235, March.
    15. Misic, V.V. & Aleman, D.M. & Sharpe, M.B., 2010. "Neighborhood search approaches to non-coplanar beam orientation optimization for total marrow irradiation using IMRT," European Journal of Operational Research, Elsevier, vol. 205(3), pages 522-527, September.
    16. Eva K. Lee, 2004. "Generating Cutting Planes for Mixed Integer Programming Problems in a Parallel Computing Environment," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 3-26, February.
    17. H. Edwin Romeijn & Ravindra K. Ahuja & James F. Dempsey & Arvind Kumar, 2006. "A New Linear Programming Approach to Radiation Therapy Treatment Planning Problems," Operations Research, INFORMS, vol. 54(2), pages 201-216, April.
    18. Ali Ajdari & Fatemeh Saberian & Archis Ghate, 2020. "A Theoretical Framework for Learning Tumor Dose-Response Uncertainty in Individualized Spatiobiologically Integrated Radiotherapy," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 930-951, October.
    19. Gino Lim & Laleh Kardar & Wenhua Cao, 2014. "A hybrid framework for optimizing beam angles in radiation therapy planning," Annals of Operations Research, Springer, vol. 217(1), pages 357-383, June.
    20. Joana Dias & Humberto Rocha & Brígida Ferreira & Maria Lopes, 2014. "A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 431-455, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:196:y:2012:i:1:p:683-705:10.1007/s10479-010-0741-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.