ArticlePhysics and imaging in radiation oncology2024
Full daily re-optimization improves plan quality during online adaptive radiotherapy.
Article in Physics and imaging in radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Online adaptive radiotherapy: International strategies for AI-enabled workflow efficiency and radiation therapist-led delivery for sustainable practice.Technical innovations & patient support in radiation oncology · 2026Article
- Development of an artificial intelligence driven dose prediction pipeline for online adaptive magnetic resonance-guided radiotherapy.Physics and imaging in radiation oncology · 2026Article
- A tandem reinforcement learning framework for localized prostate cancer treatment planning and machine parameter optimization.Medical physics · 2026Article
- Assessing population-based to personalized planning strategies for head and neck adaptive radiotherapy.Journal of applied clinical medical physics · 2025Article
- Machine learning automated treatment planning for online magnetic resonance guided adaptive radiotherapy of prostate cancer.Physics and imaging in radiation oncology · 2024Article
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Authors and funding
9 authors.
Funding
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Abstract
Background and purpose: Daily online treatment plan adaptation requires a fast workflow and planning process. Current online planning consists of adaptation of a predefined reference plan, which might be suboptimal in cases of large anatomic changes. The aim of this study was to investigate plan quality differences between the current online re-planning approach and a complete re-optimization. Material and methods: Magnetic resonance linear accelerator reference plans for ten prostate cancer patients were automatically generated using particle swarm optimization (PSO). Adapted plans were created for each fraction using (1) the current re-planning approach and (2) full PSO re-optimization and evaluated overall compliance with institutional dose-volume criteria compared to (3) clinically delivered fractions. Relative volume differences between reference and daily anatomy were assessed for planning target volumes (PTV60, PTV57.6), rectum and bladder and correlated with dose-volume results. Results: The PSO approach showed significantly higher adherence to dose-volume criteria than the reference approach and clinical fractions (p < 0.001). In 74 % of PSO plans at most one criterion failed compared to 56 % in the reference approach and 41 % in clinical plans. A fair correlation between PTV60 D98% and relative bladder volume change was observed for the reference approach. Bladder volume reductions larger than 50 % compared to the reference plan recurrently decreased PTV60 D98% below 56 Gy. Conclusion: Complete re-optimization maintained target coverage and organs at risk sparing even after large anatomic variations. Re-planning based on daily magnetic resonance imaging was sufficient for small variations, while large variations led to decreasing target coverage and organ-at-risk sparing.
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