Literature DB >> 29907505

Personalized automated treatment planning for breast plus locoregional lymph nodes using Hybrid RapidArc.

Mariët J van Duren-Koopman1, Jim P Tol2, Max Dahele1, Ewa Bucko1, Philip Meijnen1, Ben J Slotman1, Wilko F Verbakel1.   

Abstract

PURPOSE: Breast cancer patients who require locoregional lymph node (LLN) irradiation can be treated using a hybrid RapidArc technique combining 2 tangential and 3 RapidArc fields. Because the creation of hybrid RapidArc plans is complex and labor-intensive, we developed an automated treatment planning workflow using the scripting application programming interface of the Eclipse treatment planning system. METHODS AND MATERIALS: Fifteen patients (5 right- and 10 left-sided) previously treated with breast + LLN radiation therapy were replanned using the script. The automated workflow included1 optimal placement of the tangential fields based on the planning target volume and organ-at-risk contours, followed by optimization of field weights and beam energy2; positioning of the RapidArc fields; and3 subsequent RapidArc optimization using the RapidPlan knowledge-based planning solution.
RESULTS: Average total planning times were 163 ± 97 and 33 ± 5 minutes for the manual and automated plans, respectively, with approximately 130 and 5 minutes of user interaction. Dosimetrically, both sets of plans were very similar, with comparable planning target volume dose homogeneity values and organ-at-risk mean dose differences of ≤1.9 Gy. In 14/15 patients, the physician judged that the automated plan was either preferred (n = 4) or equal (n = 10) to the manual plan.
CONCLUSIONS: The complex hybrid RapidArc planning process for patients requiring breast + LLN irradiation was automated by optimizing the tangential field setup and integrating RapidPlan. The quality of the automated and manual plans was comparable, whereas automated planning times were substantially shorter. The principles described here could be used to automate other planning workflows.
Copyright © 2018 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

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Mesh:

Year:  2018        PMID: 29907505     DOI: 10.1016/j.prro.2018.03.008

Source DB:  PubMed          Journal:  Pract Radiat Oncol        ISSN: 1879-8500


  13 in total

1.  Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning.

Authors:  Roni Hytönen; Reynald Vanderstraeten; Max Dahele; Wilko F A R Verbakel
Journal:  Cancers (Basel)       Date:  2022-06-09       Impact factor: 6.575

2.  Integration of biological factors in the treatment plan evaluation in breast cancer radiotherapy.

Authors:  Henrik Svensson; Dan Lundstedt; Maria Hällje; Magnus Gustafsson; Roumiana Chakarova; Per Karlsson
Journal:  Phys Imaging Radiat Oncol       Date:  2019-08-30

3.  Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes-Internal mammary and/or supraclavicular regions.

Authors:  Maria Rago; Lorenzo Placidi; Mattia Polsoni; Giulia Rambaldi; Davide Cusumano; Francesca Greco; Luca Indovina; Sebastiano Menna; Elisa Placidi; Gerardina Stimato; Stefania Teodoli; Gian Carlo Mattiucci; Silvia Chiesa; Fabio Marazzi; Valeria Masiello; Vincenzo Valentini; Marco De Spirito; Luigi Azario
Journal:  PLoS One       Date:  2021-01-15       Impact factor: 3.240

4.  Development and evaluation of radiotherapy deep learning dose prediction models for breast cancer.

Authors:  Nienke Bakx; Hanneke Bluemink; Els Hagelaar; Maurice van der Sangen; Jacqueline Theuws; Coen Hurkmans
Journal:  Phys Imaging Radiat Oncol       Date:  2021-01-30

5.  A pilot study of machine-learning based automated planning for primary brain tumours.

Authors:  Derek S Tsang; Grace Tsui; Chris McIntosh; Thomas Purdie; Glenn Bauman; Hitesh Dama; Normand Laperriere; Barbara-Ann Millar; David B Shultz; Sameera Ahmed; Mohammad Khandwala; David C Hodgson
Journal:  Radiat Oncol       Date:  2022-01-06       Impact factor: 3.481

6.  Knowledge-Based Volumetric Modulated Arc Therapy Treatment Planning for Breast Cancer.

Authors:  Oscar Abel Apaza Blanco; María José Almada; Albin Ariel Garcia Andino; Silvia Zunino; Daniel Venencia
Journal:  J Med Phys       Date:  2021-12-02

7.  Fast, Automated, Knowledge-Based Treatment Planning for Selecting Patients for Proton Therapy Based on Normal Tissue Complication Probabilities.

Authors:  Roni Hytönen; Marije R Vergeer; Reynald Vanderstraeten; Timo K Koponen; Christel Smith; Wilko F A R Verbakel
Journal:  Adv Radiat Oncol       Date:  2022-01-28

8.  Harmonization of breast cancer radiotherapy treatment planning in the Netherlands.

Authors:  Coen Hurkmans; Cindy Duisters; Mieke Peters-Verhoeven; Liesbeth Boersma; Karolien Verhoeven; Nina Bijker; Koen Crama; Tonnis Nuver; Maurice van der Sangen
Journal:  Tech Innov Patient Support Radiat Oncol       Date:  2021-07-15

9.  Single click automated breast planning with iterative optimization.

Authors:  Ben Archibald-Heeren; Mikel Byrne; Yunfei Hu; Guilin Liu; Nick Collett; Meng Cai; Yang Wang
Journal:  J Appl Clin Med Phys       Date:  2020-10-05       Impact factor: 2.243

10.  Comparison of volumetric modulated arc therapy and intensity-modulated radiotherapy for left-sided whole-breast irradiation using automated planning.

Authors:  L Redapi; L Rossi; L Marrazzo; J J Penninkhof; S Pallotta; B Heijmen
Journal:  Strahlenther Onkol       Date:  2021-08-05       Impact factor: 3.621

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