Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer

Background and purpose: Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain plan quality consistency. The purpose of this study w...

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Autores principales:	Dennis van de Sande, Marjan Sharabiani, Hanneke Bluemink, Esther Kneepkens, Nienke Bakx, Els Hagelaar, Maurice van der Sangen, Jacqueline Theuws, Coen Hurkmans
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Locally advanced breast cancer Convolutional neural networks Dose prediction Dose mimicking Machine learning Medical physics. Medical radiology. Nuclear medicine R895-920 Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/07caacea83cd4701b752341561a171a3
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spelling	oai:doaj.org-article:07caacea83cd4701b752341561a171a32021-12-04T04:35:22ZArtificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer2405-631610.1016/j.phro.2021.11.007https://doaj.org/article/07caacea83cd4701b752341561a171a32021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405631621000713https://doaj.org/toc/2405-6316Background and purpose: Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain plan quality consistency. The purpose of this study was to create treatment plans for locally advanced breast cancer patients using a Convolutional Neural Network (CNN). Materials and methods: Data of 60 patients treated for left-sided breast cancer was used with a training, validation and test split of 36/12/12, respectively. The in-house built CNN model was a hierarchically densely connected U-net (HD U-net). The inputs for the HD U-net were 2D distance maps of the relevant regions of interest. Dose predictions, generated by the HD U-net, were used for a mimicking algorithm in order to create clinically deliverable plans. Results: Dose predictions were generated by the HD U-net and mimicked using a commercial treatment planning system. The predicted plans fulfilling all clinical goals while showing small (≤0.5 Gy) statistically significant differences (p < 0.05) in the doses compared to the manual plans. The mimicked plans show statistically significant differences in the average doses for the heart and lung of ≤0.5 Gy and a reduced D2% of all PTVs. In total, ten of the twelve mimicked plans were clinically acceptable. Conclusions: We created a CNN model which can generate clinically acceptable plans for left-sided locally advanced breast cancer patients. This model shows great potential to speed up the treatment planning process while maintaining consistent plan quality.Dennis van de SandeMarjan SharabianiHanneke BlueminkEsther KneepkensNienke BakxEls HagelaarMaurice van der SangenJacqueline TheuwsCoen HurkmansElsevierarticleLocally advanced breast cancerConvolutional neural networksDose predictionDose mimickingMachine learningMedical physics. Medical radiology. Nuclear medicineR895-920Neoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENPhysics and Imaging in Radiation Oncology, Vol 20, Iss , Pp 111-116 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Locally advanced breast cancer Convolutional neural networks Dose prediction Dose mimicking Machine learning Medical physics. Medical radiology. Nuclear medicine R895-920 Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Locally advanced breast cancer Convolutional neural networks Dose prediction Dose mimicking Machine learning Medical physics. Medical radiology. Nuclear medicine R895-920 Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Dennis van de Sande Marjan Sharabiani Hanneke Bluemink Esther Kneepkens Nienke Bakx Els Hagelaar Maurice van der Sangen Jacqueline Theuws Coen Hurkmans Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer
description	Background and purpose: Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain plan quality consistency. The purpose of this study was to create treatment plans for locally advanced breast cancer patients using a Convolutional Neural Network (CNN). Materials and methods: Data of 60 patients treated for left-sided breast cancer was used with a training, validation and test split of 36/12/12, respectively. The in-house built CNN model was a hierarchically densely connected U-net (HD U-net). The inputs for the HD U-net were 2D distance maps of the relevant regions of interest. Dose predictions, generated by the HD U-net, were used for a mimicking algorithm in order to create clinically deliverable plans. Results: Dose predictions were generated by the HD U-net and mimicked using a commercial treatment planning system. The predicted plans fulfilling all clinical goals while showing small (≤0.5 Gy) statistically significant differences (p < 0.05) in the doses compared to the manual plans. The mimicked plans show statistically significant differences in the average doses for the heart and lung of ≤0.5 Gy and a reduced D2% of all PTVs. In total, ten of the twelve mimicked plans were clinically acceptable. Conclusions: We created a CNN model which can generate clinically acceptable plans for left-sided locally advanced breast cancer patients. This model shows great potential to speed up the treatment planning process while maintaining consistent plan quality.
format	article
author	Dennis van de Sande Marjan Sharabiani Hanneke Bluemink Esther Kneepkens Nienke Bakx Els Hagelaar Maurice van der Sangen Jacqueline Theuws Coen Hurkmans
author_facet	Dennis van de Sande Marjan Sharabiani Hanneke Bluemink Esther Kneepkens Nienke Bakx Els Hagelaar Maurice van der Sangen Jacqueline Theuws Coen Hurkmans
author_sort	Dennis van de Sande
title	Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer
title_short	Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer
title_full	Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer
title_fullStr	Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer
title_full_unstemmed	Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer
title_sort	artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/07caacea83cd4701b752341561a171a3
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Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer

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