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Automated treatment planning of postmastectomy radiotherapy

PURPOSE: Breast cancer is the most common cancer in women globally and radiation therapy is a cornerstone of its treatment. However, there is an enormous shortage of radiotherapy staff, especially in low‐ and middle‐income countries. This shortage could be ameliorated through increased automation in...

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Autores principales: Kisling, Kelly, Zhang, Lifei, Shaitelman, Simona F., Anderson, David, Thebe, Tselane, Yang, Jinzhong, Balter, Peter A., Howell, Rebecca M., Jhingran, Anuja, Schmeler, Kathleen, Simonds, Hannah, du Toit, Monique, Trauernicht, Christoph, Burger, Hester, Botha, Kobus, Joubert, Nanette, Beadle, Beth M., Court, Laurence
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6739169/
https://www.ncbi.nlm.nih.gov/pubmed/31077593
http://dx.doi.org/10.1002/mp.13586
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author Kisling, Kelly
Zhang, Lifei
Shaitelman, Simona F.
Anderson, David
Thebe, Tselane
Yang, Jinzhong
Balter, Peter A.
Howell, Rebecca M.
Jhingran, Anuja
Schmeler, Kathleen
Simonds, Hannah
du Toit, Monique
Trauernicht, Christoph
Burger, Hester
Botha, Kobus
Joubert, Nanette
Beadle, Beth M.
Court, Laurence
author_facet Kisling, Kelly
Zhang, Lifei
Shaitelman, Simona F.
Anderson, David
Thebe, Tselane
Yang, Jinzhong
Balter, Peter A.
Howell, Rebecca M.
Jhingran, Anuja
Schmeler, Kathleen
Simonds, Hannah
du Toit, Monique
Trauernicht, Christoph
Burger, Hester
Botha, Kobus
Joubert, Nanette
Beadle, Beth M.
Court, Laurence
author_sort Kisling, Kelly
collection PubMed
description PURPOSE: Breast cancer is the most common cancer in women globally and radiation therapy is a cornerstone of its treatment. However, there is an enormous shortage of radiotherapy staff, especially in low‐ and middle‐income countries. This shortage could be ameliorated through increased automation in the radiation treatment planning process, which may reduce the workload on radiotherapy staff and improve efficiency in preparing radiotherapy treatments for patients. To this end, we sought to create an automated treatment planning tool for postmastectomy radiotherapy (PMRT). METHODS: Algorithms to automate every step of PMRT planning were developed and integrated into a commercial treatment planning system. The only required inputs for automated PMRT planning are a planning computed tomography scan, a plan directive, and selection of the inferior border of the tangential fields. With no other human input, the planning tool automatically creates a treatment plan and presents it for review. The major automated steps are (a) segmentation of relevant structures (targets, normal tissues, and other planning structures), (b) setup of the beams (tangential fields matched with a supraclavicular field), and (c) optimization of the dose distribution by using a mix of high‐ and low‐energy photon beams and field‐in‐field modulation for the tangential fields. This automated PMRT planning tool was tested with ten computed tomography scans of patients with breast cancer who had received irradiation of the left chest wall. These plans were assessed quantitatively using their dose distributions and were reviewed by two physicians who rated them on a three‐tiered scale: use as is, minor changes, or major changes. The accuracy of the automated segmentation of the heart and ipsilateral lung was also assessed. Finally, a plan quality verification tool was tested to alert the user to any possible deviations in the quality of the automatically created treatment plans. RESULTS: The automatically created PMRT plans met the acceptable dose objectives, including target coverage, maximum plan dose, and dose to organs at risk, for all but one patient for whom the heart objectives were exceeded. Physicians accepted 50% of the treatment plans as is and required only minor changes for the remaining 50%, which included the one patient whose plan had a high heart dose. Furthermore, the automatically segmented contours of the heart and ipsilateral lung agreed well with manually edited contours. Finally, the automated plan quality verification tool detected 92% of the changes requested by physicians in this review. CONCLUSIONS: We developed a new tool for automatically planning PMRT for breast cancer, including irradiation of the chest wall and ipsilateral lymph nodes (supraclavicular and level III axillary). In this initial testing, we found that the plans created by this tool are clinically viable, and the tool can alert the user to possible deviations in plan quality. The next step is to subject this tool to prospective testing, in which automatically planned treatments will be compared with manually planned treatments.
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spelling pubmed-67391692019-10-03 Automated treatment planning of postmastectomy radiotherapy Kisling, Kelly Zhang, Lifei Shaitelman, Simona F. Anderson, David Thebe, Tselane Yang, Jinzhong Balter, Peter A. Howell, Rebecca M. Jhingran, Anuja Schmeler, Kathleen Simonds, Hannah du Toit, Monique Trauernicht, Christoph Burger, Hester Botha, Kobus Joubert, Nanette Beadle, Beth M. Court, Laurence Med Phys THERAPEUTIC INTERVENTIONS PURPOSE: Breast cancer is the most common cancer in women globally and radiation therapy is a cornerstone of its treatment. However, there is an enormous shortage of radiotherapy staff, especially in low‐ and middle‐income countries. This shortage could be ameliorated through increased automation in the radiation treatment planning process, which may reduce the workload on radiotherapy staff and improve efficiency in preparing radiotherapy treatments for patients. To this end, we sought to create an automated treatment planning tool for postmastectomy radiotherapy (PMRT). METHODS: Algorithms to automate every step of PMRT planning were developed and integrated into a commercial treatment planning system. The only required inputs for automated PMRT planning are a planning computed tomography scan, a plan directive, and selection of the inferior border of the tangential fields. With no other human input, the planning tool automatically creates a treatment plan and presents it for review. The major automated steps are (a) segmentation of relevant structures (targets, normal tissues, and other planning structures), (b) setup of the beams (tangential fields matched with a supraclavicular field), and (c) optimization of the dose distribution by using a mix of high‐ and low‐energy photon beams and field‐in‐field modulation for the tangential fields. This automated PMRT planning tool was tested with ten computed tomography scans of patients with breast cancer who had received irradiation of the left chest wall. These plans were assessed quantitatively using their dose distributions and were reviewed by two physicians who rated them on a three‐tiered scale: use as is, minor changes, or major changes. The accuracy of the automated segmentation of the heart and ipsilateral lung was also assessed. Finally, a plan quality verification tool was tested to alert the user to any possible deviations in the quality of the automatically created treatment plans. RESULTS: The automatically created PMRT plans met the acceptable dose objectives, including target coverage, maximum plan dose, and dose to organs at risk, for all but one patient for whom the heart objectives were exceeded. Physicians accepted 50% of the treatment plans as is and required only minor changes for the remaining 50%, which included the one patient whose plan had a high heart dose. Furthermore, the automatically segmented contours of the heart and ipsilateral lung agreed well with manually edited contours. Finally, the automated plan quality verification tool detected 92% of the changes requested by physicians in this review. CONCLUSIONS: We developed a new tool for automatically planning PMRT for breast cancer, including irradiation of the chest wall and ipsilateral lymph nodes (supraclavicular and level III axillary). In this initial testing, we found that the plans created by this tool are clinically viable, and the tool can alert the user to possible deviations in plan quality. The next step is to subject this tool to prospective testing, in which automatically planned treatments will be compared with manually planned treatments. John Wiley and Sons Inc. 2019-07-09 2019-09 /pmc/articles/PMC6739169/ /pubmed/31077593 http://dx.doi.org/10.1002/mp.13586 Text en © 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle THERAPEUTIC INTERVENTIONS
Kisling, Kelly
Zhang, Lifei
Shaitelman, Simona F.
Anderson, David
Thebe, Tselane
Yang, Jinzhong
Balter, Peter A.
Howell, Rebecca M.
Jhingran, Anuja
Schmeler, Kathleen
Simonds, Hannah
du Toit, Monique
Trauernicht, Christoph
Burger, Hester
Botha, Kobus
Joubert, Nanette
Beadle, Beth M.
Court, Laurence
Automated treatment planning of postmastectomy radiotherapy
title Automated treatment planning of postmastectomy radiotherapy
title_full Automated treatment planning of postmastectomy radiotherapy
title_fullStr Automated treatment planning of postmastectomy radiotherapy
title_full_unstemmed Automated treatment planning of postmastectomy radiotherapy
title_short Automated treatment planning of postmastectomy radiotherapy
title_sort automated treatment planning of postmastectomy radiotherapy
topic THERAPEUTIC INTERVENTIONS
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6739169/
https://www.ncbi.nlm.nih.gov/pubmed/31077593
http://dx.doi.org/10.1002/mp.13586
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