ArticleJournal of applied clinical medical physics2025
Benefits of robust optimization in comparison with PTV-based planning approach to overcome beam positioning uncertainties in spot-scanning proton therapy.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- MRI and PET Alterations in Adult Skull Base Tumors: A Narrative Review of Proton Versus Photon Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Review
- Benefits of robust optimization in comparison with PTV-based planning approach to overcome beam positioning uncertainties in spot-scanning proton therapy.Journal of applied clinical medical physics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
background and purposeThe inherent uncertainties associated with proton therapy (PT) may present a challenge to the optimal clinical utilization of this radiotherapy modality. The possibility to overcome the uncertainties associated with temporal fluctuations in some parameters of the PT system by selecting an additional clinical target volume to planning parget volume (CTV-PTV) margin or by modifying the planning approach was suggested. The objective of this work was to verify the sufficiency of the calculated additional margin for overcoming beam positioning uncertainties in spot-scanning PT.
methodsTreatment plans for eight patients were calculated using two methods (robust optimization and PTV-based approaches) and evaluated with regard to dose-volume histogram (DVH)-based dosimetric metrics. The robustness of each plan was evaluated under six scenarios in linear directions ±4 mm and two scenarios with a ±3.5% range uncertainty. A comparison between robust optimization and PTV-based approaches was performed in the context of additional 1-mm setup uncertainty associated with facility-specific beam uncertainties.
resultsNo statistically significant difference was identified between the initial plans of the PTV-based and robust approaches with regard to investigated dosimetric metrics. But robust optimization shows better results in comparison to PTV approach in majority of investigated scenarios. CTV was fully encompassed by the 100% dose in the plans created using the robust optimization approach. The Robust approach demonstrated superior dose conformity to the CTV, both for the 100% and 95% prescribed doses. Although both the PTV and Robust approaches permitted homogeneity in all scenarios, the superiority of the Robust approach was evident.
conclusionsIt is necessary to consider the facility-specific beam uncertainty as additional margin during treatment planning in PT. It was demonstrated that the robust optimization approach is an effective method for eliminating beam-related facility-specific uncertainties order of 1 mm in comparison to the PTV planning approach.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.