ArticleCureus2024
Impacts of Calculation Grid Spacing and Statistical Uncertainty of Monte Carlo Algorithm on Stereotactic Radiosurgery Planning With Volumetric-Modulated Arcs for Single Brain Metastases Using the Monaco® System.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Article
- Article
- Impact of Voxel Grid Size and Statistical Uncertainty on Surface Depth Dose Via Various Planning Techniques and Immobilization Devices Using Monte Carlo Algorithm.Technology in cancer research & treatmentArticle
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose In linac-based stereotactic radiosurgery (SRS) utilizing a multileaf collimator (MLC) for brain metastases (BMs), a volumetric-modulated arc (VMA) technique is indispensable for generating a suitable dose distribution with efficient planning and delivery. However, the optimal calculation grid spacing (GS) and statistical uncertainty (SU) of the Monte Carlo algorithm for VMA optimization have yet to be determined. This planning study aimed to examine the impacts of GS and GU settings on VMA-based SRS planning and to find the optimal combination for templating. Materials and methods Thirty clinical BMs with a gross tumor volume (GTV) of 0.08-48.09 cc (median 9.81 cc) were included. The treatment platform included a 5-mm leaf-width MLC Agility
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