IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v52y2022i1p69-89.html
   My bibliography  Save this article

Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy

Author

Listed:
  • Masoud Zarepisheh

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Linda Hong

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Ying Zhou

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Qijie Huang

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Jie Yang

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Gourav Jhanwar

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Hai D. Pham

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Pınar Dursun

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Pengpeng Zhang

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Margie A. Hunt

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Gig S. Mageras

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Jonathan T. Yang

    (Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Yoshiya (Josh) Yamada

    (Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

  • Joseph O. Deasy

    (Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065)

Abstract

Each year, approximately 18 million new cancer cases are diagnosed worldwide, and about half must be treated with radiotherapy. A successful treatment requires treatment planning with the customization of penetrating radiation beams to sterilize cancerous cells without harming nearby normal organs and tissues. This process currently involves extensive manual tuning of parameters by an expert planner, making it a time-consuming and labor-intensive process, with quality and immediacy of critical care dependent on the planner’s expertise. To improve the speed, quality, and availability of this highly specialized care, Memorial Sloan Kettering Cancer Center developed and applied advanced optimization tools to this problem (e.g., using hierarchical constrained optimization, convex approximations, and Lagrangian methods). This resulted in both a greatly improved radiotherapy treatment planning process and the generation of reliable and consistent high-quality plans that reflect clinical priorities. These improved techniques have been the foundation of high-quality treatments and have positively impacted over 5,000 patients to date, including numerous patients in severe pain and in urgent need of treatment who might have otherwise required longer hospital stays or undergone unnecessary surgery to control the progression of their disease. We expect that the wide distribution of the system we developed will ultimately impact patient care more broadly, including in resource-constrained countries.

Suggested Citation

  • Masoud Zarepisheh & Linda Hong & Ying Zhou & Qijie Huang & Jie Yang & Gourav Jhanwar & Hai D. Pham & Pınar Dursun & Pengpeng Zhang & Margie A. Hunt & Gig S. Mageras & Jonathan T. Yang & Yoshiya (Josh), 2022. "Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy," Interfaces, INFORMS, vol. 52(1), pages 69-89, January.
    • Handle: RePEc:inm:orinte:v:52:y:2022:i:1:p:69-89
    DOI: 10.1287/inte.2021.1095
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2021.1095
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2021.1095?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
    ---><---

    References listed on IDEAS

    as
    1. H. Romeijn & James Dempsey, 2008. "Rejoinder on: Intensity modulated radiation therapy treatment plan optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 256-257, December.
    2. H. Romeijn & James Dempsey, 2008. "Intensity modulated radiation therapy treatment plan optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 215-243, December.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    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. Danielle A. Ripsman & Thomas G. Purdie & Timothy C. Y. Chan & Houra Mahmoudzadeh, 2022. "Robust Direct Aperture Optimization for Radiation Therapy Treatment Planning," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2017-2038, July.
    2. Ali Adibi & Ehsan Salari, 2022. "Scalable Optimization Methods for Incorporating Spatiotemporal Fractionation into Intensity-Modulated Radiotherapy Planning," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1240-1256, March.
    3. Ehsan Salari & H. Edwin Romeijn, 2012. "Quantifying the Trade-off Between IMRT Treatment Plan Quality and Delivery Efficiency Using Direct Aperture Optimization," INFORMS Journal on Computing, INFORMS, vol. 24(4), pages 518-533, November.
    4. Rennen, G. & van Dam, E.R. & den Hertog, D., 2009. "Enhancement of Sandwich Algorithms for Approximating Higher Dimensional Convex Pareto Sets," Other publications TiSEM e2255959-6691-4ef1-88a4-5, Tilburg University, School of Economics and Management.
    5. 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.
    6. Chan, Timothy C.Y. & Mišić, Velibor V., 2013. "Adaptive and robust radiation therapy optimization for lung cancer," European Journal of Operational Research, Elsevier, vol. 231(3), pages 745-756.
    7. Fatemeh Saberian & Archis Ghate & Minsun Kim, 2017. "Spatiotemporally Optimal Fractionation in Radiotherapy," INFORMS Journal on Computing, INFORMS, vol. 29(3), pages 422-437, August.
    8. Gijs Rennen & Edwin R. van Dam & Dick den Hertog, 2011. "Enhancement of Sandwich Algorithms for Approximating Higher-Dimensional Convex Pareto Sets," INFORMS Journal on Computing, INFORMS, vol. 23(4), pages 493-517, November.
    9. Rasmus Bokrantz & Anders Forsgren, 2013. "An Algorithm for Approximating Convex Pareto Surfaces Based on Dual Techniques," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 377-393, May.
    10. Luke Mason & Vicky Mak-Hau & Andreas Ernst, 2015. "A parallel optimisation approach for the realisation problem in intensity modulated radiotherapy treatment planning," Computational Optimization and Applications, Springer, vol. 60(2), pages 441-477, March.
    11. Oylum S¸eker & Mucahit Cevik & Merve Bodur & Young Lee & Mark Ruschin, 2023. "A Multiobjective Approach for Sector Duration Optimization in Stereotactic Radiosurgery Treatment Planning," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 248-264, January.
    12. Salazar-González, Juan-José, 2021. "Designing optimal masks for a multi-object spectrometer," Omega, Elsevier, vol. 103(C).
    13. Jason Xu & Eric C. Chi & Meng Yang & Kenneth Lange, 2018. "A majorization–minimization algorithm for split feasibility problems," Computational Optimization and Applications, Springer, vol. 71(3), pages 795-828, December.
    14. Shraddha Ghatkar, 2019. "Optimization of fractionation schemes and beamlet intensities in intensity-modulated radiation therapy with changing cancer tumor properties," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 46(4), pages 385-407, December.
    15. Kelsey Maass & Minsun Kim & Aleksandr Aravkin, 2022. "A Nonconvex Optimization Approach to IMRT Planning with Dose–Volume Constraints," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1366-1386, May.
    16. 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.
    17. 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.
    18. Raith, Andrea & Ehrgott, Matthias & Fauzi, Fariza & Lin, Kuan-Min & Macann, Andrew & Rouse, Paul & Simpson, John, 2022. "Integrating Data Envelopment Analysis into radiotherapy treatment planning for head and neck cancer patients," European Journal of Operational Research, Elsevier, vol. 296(1), pages 289-303.
    19. Lim, Gino J. & Cao, Wenhua, 2012. "A two-phase method for selecting IMRT treatment beam angles: Branch-and-Prune and local neighborhood search," European Journal of Operational Research, Elsevier, vol. 217(3), pages 609-618.

    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:inm:orinte:v:52:y:2022:i:1:p:69-89. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.