ArticleCureus2024
Determining Simple and Effective Cost Functions for an Efficient Volumetric-Modulated Arcs-Based Stereotactic Radiosurgery for Single Brain Metastases Using Monaco® Planning 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 8 papers.
What it found
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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.
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
8 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Volumetric-modulated arcs (VMA) can produce dose distributions suitable for stereotactic radiosurgery (SRS) with a multi-leaf collimator (MLC) for brain metastases (BMs). The treatment planning and verification for VMA are more complicated than for dynamic conformal arcs. The longer the preparation time from image acquisition to the start of irradiation, the higher the risk of tumor growth and/or displacement. This planning study aimed to exploit the simple and effective cost function (CF) for establishing semi-automatic efficient VMA optimization for SRS of single BMs. Materials and methods The study population included 30 clinical BMs with a gross tumor volume (GTV) of 0.72-44.30 cc (median 9.81 cc) and a depth of 20-79 mm (median 41 mm). The treatment platform included a 5-mm leaf-width MLC Agility
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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.