Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy

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) Yamada () and Joseph O. Deasy ()
Additional contact information
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

Interfaces, 2022, vol. 52, issue 1, 69-89

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.

Keywords: radiotherapy cancer treatment planning; intensity-modulated radiation therapy; hierarchical optimization; multicriteria optimization; large-scale optimization; mixed-integer nonlinear programming; Edelman Award (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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