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MR-Linac Radiotherapy – The Beam Angle Selection Problem

BACKGROUND: With the large-scale introduction of volumetric modulated arc therapy (VMAT), selection of optimal beam angles for coplanar static-beam IMRT has increasingly become obsolete. Due to unavailability of VMAT in current MR-linacs, the problem has re-gained importance. An application for auto...

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Autores principales: Bijman, Rik, Rossi, Linda, Janssen, Tomas, de Ruiter, Peter, van Triest, Baukelien, Breedveld, Sebastiaan, Sonke, Jan-Jakob, Heijmen, Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518312/
https://www.ncbi.nlm.nih.gov/pubmed/34660281
http://dx.doi.org/10.3389/fonc.2021.717681
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author Bijman, Rik
Rossi, Linda
Janssen, Tomas
de Ruiter, Peter
van Triest, Baukelien
Breedveld, Sebastiaan
Sonke, Jan-Jakob
Heijmen, Ben
author_facet Bijman, Rik
Rossi, Linda
Janssen, Tomas
de Ruiter, Peter
van Triest, Baukelien
Breedveld, Sebastiaan
Sonke, Jan-Jakob
Heijmen, Ben
author_sort Bijman, Rik
collection PubMed
description BACKGROUND: With the large-scale introduction of volumetric modulated arc therapy (VMAT), selection of optimal beam angles for coplanar static-beam IMRT has increasingly become obsolete. Due to unavailability of VMAT in current MR-linacs, the problem has re-gained importance. An application for automated IMRT treatment planning with integrated, patient-specific computer-optimization of beam angles (BAO) was used to systematically investigate computer-aided generation of beam angle class solutions (CS) for replacement of computationally expensive patient-specific BAO. Rectal cancer was used as a model case. MATERIALS AND METHODS: 23 patients treated at a Unity MR-linac were included. BAO(x) plans (x=7-12 beams) were generated for all patients. Analyses of BAO(12) plans resulted in CS(x) class solutions. BAO(x) plans, CS(x) plans, and plans with equi-angular setups (EQUI(x), x=9-56) were mutually compared. RESULTS: For x>7, plan quality for CS(x) and BAO(x) was highly similar, while both were superior to EQUI(x). E.g. with CS(9), bowel/bladder D(mean) reduced by 22% [11%, 38%] compared to EQUI(9) (p<0.001). For equal plan quality, the number of EQUI beams had to be doubled compared to BAO and CS. CONCLUSIONS: Computer-generated beam angle CS could replace individualized BAO without loss in plan quality, while reducing planning complexity and calculation times, and resulting in a simpler clinical workflow. CS and BAO largely outperformed equi-angular treatment. With the developed CS, time consuming beam angle re-optimization in daily adaptive MR-linac treatment could be avoided. Further systematic research on computerized development of beam angle class solutions for MR-linac treatment planning is warranted.
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spelling pubmed-85183122021-10-16 MR-Linac Radiotherapy – The Beam Angle Selection Problem Bijman, Rik Rossi, Linda Janssen, Tomas de Ruiter, Peter van Triest, Baukelien Breedveld, Sebastiaan Sonke, Jan-Jakob Heijmen, Ben Front Oncol Oncology BACKGROUND: With the large-scale introduction of volumetric modulated arc therapy (VMAT), selection of optimal beam angles for coplanar static-beam IMRT has increasingly become obsolete. Due to unavailability of VMAT in current MR-linacs, the problem has re-gained importance. An application for automated IMRT treatment planning with integrated, patient-specific computer-optimization of beam angles (BAO) was used to systematically investigate computer-aided generation of beam angle class solutions (CS) for replacement of computationally expensive patient-specific BAO. Rectal cancer was used as a model case. MATERIALS AND METHODS: 23 patients treated at a Unity MR-linac were included. BAO(x) plans (x=7-12 beams) were generated for all patients. Analyses of BAO(12) plans resulted in CS(x) class solutions. BAO(x) plans, CS(x) plans, and plans with equi-angular setups (EQUI(x), x=9-56) were mutually compared. RESULTS: For x>7, plan quality for CS(x) and BAO(x) was highly similar, while both were superior to EQUI(x). E.g. with CS(9), bowel/bladder D(mean) reduced by 22% [11%, 38%] compared to EQUI(9) (p<0.001). For equal plan quality, the number of EQUI beams had to be doubled compared to BAO and CS. CONCLUSIONS: Computer-generated beam angle CS could replace individualized BAO without loss in plan quality, while reducing planning complexity and calculation times, and resulting in a simpler clinical workflow. CS and BAO largely outperformed equi-angular treatment. With the developed CS, time consuming beam angle re-optimization in daily adaptive MR-linac treatment could be avoided. Further systematic research on computerized development of beam angle class solutions for MR-linac treatment planning is warranted. Frontiers Media S.A. 2021-10-01 /pmc/articles/PMC8518312/ /pubmed/34660281 http://dx.doi.org/10.3389/fonc.2021.717681 Text en Copyright © 2021 Bijman, Rossi, Janssen, de Ruiter, van Triest, Breedveld, Sonke and Heijmen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Bijman, Rik
Rossi, Linda
Janssen, Tomas
de Ruiter, Peter
van Triest, Baukelien
Breedveld, Sebastiaan
Sonke, Jan-Jakob
Heijmen, Ben
MR-Linac Radiotherapy – The Beam Angle Selection Problem
title MR-Linac Radiotherapy – The Beam Angle Selection Problem
title_full MR-Linac Radiotherapy – The Beam Angle Selection Problem
title_fullStr MR-Linac Radiotherapy – The Beam Angle Selection Problem
title_full_unstemmed MR-Linac Radiotherapy – The Beam Angle Selection Problem
title_short MR-Linac Radiotherapy – The Beam Angle Selection Problem
title_sort mr-linac radiotherapy – the beam angle selection problem
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518312/
https://www.ncbi.nlm.nih.gov/pubmed/34660281
http://dx.doi.org/10.3389/fonc.2021.717681
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