Abstract
Background and purpose:
In intensity modulated proton therapy (IMPT), the impact of setup errors and anatomical changes is commonly mitigated by robust optimization with population-based setup robustness (SR) settings and offline replanning. In this study we propose and evaluate an alternative approach based on daily plan selection from patient-specific pre-treatment established plan libraries (PLs). Clinical implementation of the PL strategy would be rather straightforward compared to daily online re-planning.
Materials and methods:
For 15 head-and-neck cancer patients, the planning CT was used to generate a PL with 5 plans, robustly optimized for increasing SR: 0, 1, 2, 3, 5 mm, and 3% range robustness. Repeat CTs (rCTs) and realistic setup and range uncertainty distributions were used for simulation of treatment courses for the PL approach, treatments with fixed SR (fSR3) and a trigger-based offline adaptive schedule for 3 mm SR (fSR3OfA). Daily plan selection in the PL approach was based only on recomputed dose to the CTV on the rCT.
Results:
Compared to using fSR3 and fSR3OfA, the risk of xerostomia grade ≥ II & III and dysphagia ≥ grade III were significantly reduced with the PL. For 6/15 patients the risk of xerostomia and/or dysphagia ≥ grade II could be reduced by > 2% by using PL. For the other patients, adherence to target coverage constraints was often improved. fSR3OfA resulted in significantly improved coverage compared to PL for selected patients.
Conclusion:
The proposed PL approach resulted in overall reduced NTCPs compared to fSR3 and fSR3OfA at limited cost in target coverage.
| Original language | English |
|---|---|
| Pages (from-to) | 68-75 |
| Number of pages | 8 |
| Journal | Radiotherapy and Oncology |
| Volume | 176 |
| DOIs | |
| Publication status | Published - Nov 2022 |
Bibliographical note
DisclosuresThis study was partly funded by a research grant of Varian, a Siemens Healthineers Company. The Erasmus MC Cancer Institute
also has research collaborations with Elekta AB, Stockholm, Sweden, and Accuray Inc, Sunnyvale, USA.
Publisher Copyright: © 2022 The Authors
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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