Automated and clinically optimal treatment planning for cancer radiotherapy Review
| Authors: | Zarepisheh, M.; Hong, L.; Zhou, Y.; Huang, Q.; Yang, J.; Jhanwar, G.; Pham, H. D.; Dursun, P.; Zhang, P.; Hunt, M. A.; Mageras, G. S.; Yang, J. T.; Yamada, Y.; Deasy, J. O. |
| Review Title: | Automated and clinically optimal treatment planning for cancer radiotherapy |
| 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: | imrt; therapy; optimization; radiation-therapy; intensity-modulated radiation; large-scale optimization; hierarchical optimization; multicriteria optimization; radiotherapy cancer treatment planning; mixed-integer nonlinear programming; edelman; award |
| Journal Title: | Informs Journal on Applied Analytics |
| Volume: | 52 |
| Issue: | 1 |
| ISSN: | 2644-0865 |
| Publisher: | Informs |
| Date Published: | 2022-01-01 |
| Start Page: | 69 |
| End Page: | 89 |
| Language: | English |
| ACCESSION: | WOS:000752972300007 |
| DOI: | 10.1287/inte.2021.1095 |
| PROVIDER: | wos |
| PMCID: | PMC9284667 |
| PUBMED: | 35847768 |
| Notes: | Article -- Source: Wos |
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