Cargando…
Prediction of VMAT delivery accuracy with textural features calculated from fluence maps
BACKGROUND: Comprehensively textural feature performance test from volumetric modulated arc therapy (VMAT) fluences to predict plan delivery accuracy. METHODS: A total of 240 VMAT plans for various treatment sites were analyzed, with Trilogy and TrueBeam STx systems. Fluence maps superposed fluences...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929348/ https://www.ncbi.nlm.nih.gov/pubmed/31870403 http://dx.doi.org/10.1186/s13014-019-1441-7 |
_version_ | 1783482683350843392 |
---|---|
author | Park, Jong Min Kim, Jung-in Park, So-Yeon |
author_facet | Park, Jong Min Kim, Jung-in Park, So-Yeon |
author_sort | Park, Jong Min |
collection | PubMed |
description | BACKGROUND: Comprehensively textural feature performance test from volumetric modulated arc therapy (VMAT) fluences to predict plan delivery accuracy. METHODS: A total of 240 VMAT plans for various treatment sites were analyzed, with Trilogy and TrueBeam STx systems. Fluence maps superposed fluences at each control point per plan. The textural features were the angular second moment (ASM), inverse difference moment (IDM), contrast, variance, correlation, and entropy, calculated from fluence maps using three displacement distances. Correlation analysis of textural feature performance as predictors of VMAT delivery accuracy used global gamma passing rates with MapCHECK2 and ArcCHECK dosimeters, and mechanical delivery errors calculated from machine log files. RESULTS: Spearman’s rank correlation coefficients (r) of the ASM (d = 10) to the gamma passing rates with 1%/2 mm using the MapCHECK2 were 0.358 and 0.519, respectively (p < 0.001). For the ArcCHECK, they were 0.273 (p = 0.001) and 0.259 (p = 0.009), respectively. The r-values of the ASM (d = 10) to the Trilogy and TrueBeam STx MLC errors were − 0.843 and − 0.859, respectively (p < 0.001), and those to the MU delivery errors were − 0.482 and − 0.589, respectively (p < 0.001). The ASM (d = 10) showed better performance in predicting VMAT delivery accuracy. CONCLUSIONS: The ASM (d = 10) calculated from VMAT plan fluence maps were strongly correlated with global gamma passing rates and MLC delivery errors, and can predict VMAT delivery accuracy. |
format | Online Article Text |
id | pubmed-6929348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69293482019-12-30 Prediction of VMAT delivery accuracy with textural features calculated from fluence maps Park, Jong Min Kim, Jung-in Park, So-Yeon Radiat Oncol Research BACKGROUND: Comprehensively textural feature performance test from volumetric modulated arc therapy (VMAT) fluences to predict plan delivery accuracy. METHODS: A total of 240 VMAT plans for various treatment sites were analyzed, with Trilogy and TrueBeam STx systems. Fluence maps superposed fluences at each control point per plan. The textural features were the angular second moment (ASM), inverse difference moment (IDM), contrast, variance, correlation, and entropy, calculated from fluence maps using three displacement distances. Correlation analysis of textural feature performance as predictors of VMAT delivery accuracy used global gamma passing rates with MapCHECK2 and ArcCHECK dosimeters, and mechanical delivery errors calculated from machine log files. RESULTS: Spearman’s rank correlation coefficients (r) of the ASM (d = 10) to the gamma passing rates with 1%/2 mm using the MapCHECK2 were 0.358 and 0.519, respectively (p < 0.001). For the ArcCHECK, they were 0.273 (p = 0.001) and 0.259 (p = 0.009), respectively. The r-values of the ASM (d = 10) to the Trilogy and TrueBeam STx MLC errors were − 0.843 and − 0.859, respectively (p < 0.001), and those to the MU delivery errors were − 0.482 and − 0.589, respectively (p < 0.001). The ASM (d = 10) showed better performance in predicting VMAT delivery accuracy. CONCLUSIONS: The ASM (d = 10) calculated from VMAT plan fluence maps were strongly correlated with global gamma passing rates and MLC delivery errors, and can predict VMAT delivery accuracy. BioMed Central 2019-12-23 /pmc/articles/PMC6929348/ /pubmed/31870403 http://dx.doi.org/10.1186/s13014-019-1441-7 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Park, Jong Min Kim, Jung-in Park, So-Yeon Prediction of VMAT delivery accuracy with textural features calculated from fluence maps |
title | Prediction of VMAT delivery accuracy with textural features calculated from fluence maps |
title_full | Prediction of VMAT delivery accuracy with textural features calculated from fluence maps |
title_fullStr | Prediction of VMAT delivery accuracy with textural features calculated from fluence maps |
title_full_unstemmed | Prediction of VMAT delivery accuracy with textural features calculated from fluence maps |
title_short | Prediction of VMAT delivery accuracy with textural features calculated from fluence maps |
title_sort | prediction of vmat delivery accuracy with textural features calculated from fluence maps |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929348/ https://www.ncbi.nlm.nih.gov/pubmed/31870403 http://dx.doi.org/10.1186/s13014-019-1441-7 |
work_keys_str_mv | AT parkjongmin predictionofvmatdeliveryaccuracywithtexturalfeaturescalculatedfromfluencemaps AT kimjungin predictionofvmatdeliveryaccuracywithtexturalfeaturescalculatedfromfluencemaps AT parksoyeon predictionofvmatdeliveryaccuracywithtexturalfeaturescalculatedfromfluencemaps |