ArticleMedical physics2022
Development of a Monte Carlo based robustness calculation and evaluation tool.
Article in Medical physics, 2022. 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, 10 citations in OpenAlex.
- DeepSMCP - Deep-learning powered denoising of Monte Carlo dose distributions within the Swiss Monte Carlo Plan.Zeitschrift fur medizinische Physik · 2026Article
- Robustness assessment of radiotherapy treatment plans in Switzerland.Zeitschrift fur medizinische Physik · 2026Article
- Monte Carlo dose calculation for photon and electron radiotherapy on dynamically deforming anatomy.Medical physics · 2025Article
- Robustness analysis of dynamic trajectory radiotherapy and volumetric modulated arc therapy plans for head and neck cancer.Physics and imaging in radiation oncology · 2024Article
- Development of a Monte Carlo based robustness calculation and evaluation tool.Medical physics · 2022Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundEvaluating plan robustness is a key step in radiotherapy. PURPOSE: To develop a flexible Monte Carlo (MC)-based robustness calculation and evaluation tool to assess and quantify dosimetric robustness of intensity-modulated radiotherapy (IMRT) treatment plans by exploring the impact of systematic and random uncertainties resulting from patient setup, patient anatomy changes, and mechanical limitations of machine components.
methodsThe robustness tool consists of two parts: the first part includes automated MC dose calculation of multiple user-defined uncertainty scenarios to populate a robustness space. An uncertainty scenario is defined by a certain combination of uncertainties in patient setup, rigid intrafraction motion and in mechanical steering of the following machine components: angles of gantry, collimator, table-yaw, table-pitch, table-roll, translational positions of jaws, multileaf-collimator (MLC) banks, and single MLC leaves. The Swiss Monte Carlo Plan (SMCP) is integrated in this tool to serve as the backbone for the MC dose calculations incorporating the uncertainties. The calculated dose distributions serve as input for the second part of the tool, handling the quantitative evaluation of the dosimetric impact of the uncertainties. A graphical user interface (GUI) is developed to simultaneously evaluate the uncertainty scenarios according to user-specified conditions based on dose-volume histogram (DVH) parameters, fast and exact gamma analysis, and dose differences. Additionally, a robustness index (RI) is introduced with the aim to simultaneously evaluate and condense dosimetric robustness against multiple uncertainties into one number. The RI is defined as the ratio of scenarios passing the conditions on the dose distributions. Weighting of the scenarios in the robustness space is possible to consider their likelihood of occurrence. The robustness tool is applied on IMRT, a volumetric modulated arc therapy (VMAT), a dynamic trajectory radiotherapy (DTRT), and a dynamic mixed beam radiotherapy (DYMBER) plan for a brain case to evaluate the robustness to uncertainties of gantry-, table-, collimator angle, MLC, and intrafraction motion. Additionally, the robustness of the IMRT, VMAT, and DTRT plan against patient setup uncertainties are compared. The robustness tool is validated by Delta4 measurements for scenarios including all uncertainty types available.
resultsThe robustness tool performs simultaneous calculation of uncertainty scenarios, and the GUI enables their fast evaluation. For all evaluated plans and uncertainties, the planning target volume (PTV) margin prevented major clinical target volume (CTV) coverage deterioration (maximum observed standard deviation of
conclusionsThe robustness tool was successfully implemented. Calculation and evaluation of uncertainty scenarios with the robustness tool were demonstrated on a brain case. Effects of patient and machine-specific uncertainties and the combination thereof on the dose distribution are evaluated in a user-friendly GUI to quantitatively assess and compare treatment plans and their robustness.
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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.