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Nenoff L, Buti G, Bobić M, Lalonde A, Nesteruk KP, Winey B, Sharp GC, Sudhyadhom A, Paganetti H. Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans. Cancers (Basel) 2022; 14:cancers14163926. [PMID: 36010919 PMCID: PMC9406068 DOI: 10.3390/cancers14163926] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 01/11/2023] Open
Abstract
Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1-5 repeated images during treatment: 'reference adaptation', with structures drawn by a physician; 'single-DIR adaptation', using a single set of deformably propagated structures; 'multi-DIR adaptation', using robust planning with multiple deformed structure sets; 'conservative adaptation', using the intersection and union of all deformed structures; 'probabilistic adaptation', using the probability of a voxel belonging to the structure in the optimization weight; and 'no adaptation'. Plans were evaluated using reference structures and compared using a scoring system. The reference adaptation with physician-drawn structures performed best, and no adaptation performed the worst. For pancreas and liver patients, adaptation with a single DIR improved the plan quality over no adaptation. For HN patients, integrating structure uncertainties brought an additional benefit. If resources for manual structure corrections would prevent online adaptation, manual correction could be replaced by a fast 'plausibility check', and plans could be adapted with correction-free adaptation strategies. Including structure uncertainties in the optimization has the potential to make online adaptation more automatable.
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Affiliation(s)
- Lena Nenoff
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Correspondence:
| | - Gregory Buti
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institute of Experimental and Clinical Research (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium
| | - Mislav Bobić
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Physics, ETH Zurich, 8092 Zurich, Switzerland
| | - Arthur Lalonde
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Konrad P. Nesteruk
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Brian Winey
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Gregory Charles Sharp
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Atchar Sudhyadhom
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Harald Paganetti
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
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