{"ID":2884163,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07368","arxiv_id":"2508.07368","title":"A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning","abstract":"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.","short_abstract":"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 u...","url_abs":"https://arxiv.org/abs/2508.07368","url_pdf":"https://arxiv.org/pdf/2508.07368v1","authors":"[\"Zihang Qiu\",\"Ali Ajdari\",\"Mislav Bobić\",\"Thomas Bortfeld\",\"Dick den Hertog\",\"Jannis Kurtz\",\"Hoyeon Lee\"]","published":"2025-08-10T14:36:23Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}