ArticleFrontiers in oncology2022
Comparison of 3DCRT and IMRT out-of-field doses in pediatric patients using Monte Carlo simulations with treatment planning system calculations and measurements.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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
0 citing papers in PubMed, 2 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
3DCRT and IMRT out-of-field doses in pediatric patients were compared using Monte Carlo simulations with treatment planning system calculations and measurements. Purpose: Out-of-field doses are given to healthy tissues, which may allow the development of second tumors. The use of IMRT in pediatric patients has been discussed, as it leads to a "bath" of low doses to large volumes of out-of-field organs and tissues. This study aims to compare out-of-field doses in pediatric patients comparing IMRT and 3DCRT techniques using measurements, Monte Carlo (MC) simulations, and treatment planning system (TPS) calculations. Materials and methods: A total dose of 54 Gy was prescribed to a PTV in the brain of a pediatric anthropomorphic phantom, for both techniques. To assess the out-of-field organ doses for both techniques, two treatment plans were performed with the 3DCRT and IMRT techniques in TPS. Measurements were carried out in a LINAC using a pediatric anthropomorphic phantom and thermoluminescent dosimeters to recreate the treatment plans, previously performed in the TPS. A computational model of a LINAC, the associated multileaf collimators, and a voxelized pediatric phantom implemented in the Monte Carlo N-Particle 6.1 computer program were also used to perform MC simulations of the out-of-field organ doses, for both techniques. Results: The results obtained by measurements and MC simulations indicate a significant increase in dose using the IMRT technique when compared to the 3DCRT technique. More specifically, measurements show higher doses with IMRT, namely, in right eye (13,041 vs. 593 mGy), left eye (6,525 vs. 475 mGy), thyroid (79 vs. 70 mGy), right lung (37 vs. 28 mGy), left lung (27 vs. 20 mGy), and heart (31 vs. 25 mGy). The obtained results indicate that out-of-field doses can be seriously underestimated by TPS. Discussion: This study presents, for the first time, out-of-field dose measurements in a realistic scenario and calculations for IMRT, centered on a voxelized pediatric phantom and an MC model of a medical LINAC, including MLC with log file-based simulations. The results pinpoint significant discrepancies in out-of-field doses for the two techniques and are a cause of concern because TPS calculations cannot accurately predict such doses. The obtained doses may presumably increase the risk of development of second tumors.
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.