A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913983001264128 |
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| author | Qiu, Zihang Ajdari, Ali Bobić, Mislav Bortfeld, Thomas Hertog, Dick den Kurtz, Jannis Lee, Hoyeon |
| author_facet | Qiu, Zihang Ajdari, Ali Bobić, Mislav Bortfeld, Thomas Hertog, Dick den Kurtz, Jannis Lee, Hoyeon |
| contents | Uncertainties such as setup and range errors can significantly compromise proton therapy. A discrete uncertainty set is often constructed to represent different uncertainty scenarios. A min-max robust optimization approach is then utilized to optimize the worst-case performance of a radiation therapy plan against the uncertainty set. However, the min-max approach can be too conservative as a single plan has to account for the entire uncertainty set. K-adaptability is a novel approach to robust optimization which covers the uncertainty set with multiple (K) solutions, reducing the conservativeness. Solving K-adaptability to optimality is known to be computationally intractable. To that end, we developed a novel and efficient K-adaptability heuristic that iteratively clusters the scenarios based on plan-scenario performance for the proton radiation therapy planning problem. Compared to the conventional robust solution, the developed K-adaptability heuristic increased the worst-case CTV Dmin dose up to 4.52 Gy on average across five head and neck patients. The developed heuristic also demonstrated its superiority in objective value and time-efficiency compared to the competing methods we tested. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07368 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning Qiu, Zihang Ajdari, Ali Bobić, Mislav Bortfeld, Thomas Hertog, Dick den Kurtz, Jannis Lee, Hoyeon Optimization and Control Uncertainties such as setup and range errors can significantly compromise proton therapy. A discrete uncertainty set is often constructed to represent different uncertainty scenarios. A min-max robust optimization approach is then utilized to optimize the worst-case performance of a radiation therapy plan against the uncertainty set. However, the min-max approach can be too conservative as a single plan has to account for the entire uncertainty set. K-adaptability is a novel approach to robust optimization which covers the uncertainty set with multiple (K) solutions, reducing the conservativeness. Solving K-adaptability to optimality is known to be computationally intractable. To that end, we developed a novel and efficient K-adaptability heuristic that iteratively clusters the scenarios based on plan-scenario performance for the proton radiation therapy planning problem. Compared to the conventional robust solution, the developed K-adaptability heuristic increased the worst-case CTV Dmin dose up to 4.52 Gy on average across five head and neck patients. The developed heuristic also demonstrated its superiority in objective value and time-efficiency compared to the competing methods we tested. |
| title | A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2508.07368 |