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Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer

PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone‐beam CT (CBCT) can provide these daily images, but x‐ray scattering limits CBCT‐image quality and hampers dose calculation accuracy. The purpose of this study was to genera...

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Autores principales: Thummerer, Adrian, Seller Oria, Carmen, Zaffino, Paolo, Meijers, Arturs, Guterres Marmitt, Gabriel, Wijsman, Robin, Seco, Joao, Langendijk, Johannes Albertus, Knopf, Antje‐Christin, Spadea, Maria Francesca, Both, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299115/
https://www.ncbi.nlm.nih.gov/pubmed/34725829
http://dx.doi.org/10.1002/mp.15333
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author Thummerer, Adrian
Seller Oria, Carmen
Zaffino, Paolo
Meijers, Arturs
Guterres Marmitt, Gabriel
Wijsman, Robin
Seco, Joao
Langendijk, Johannes Albertus
Knopf, Antje‐Christin
Spadea, Maria Francesca
Both, Stefan
author_facet Thummerer, Adrian
Seller Oria, Carmen
Zaffino, Paolo
Meijers, Arturs
Guterres Marmitt, Gabriel
Wijsman, Robin
Seco, Joao
Langendijk, Johannes Albertus
Knopf, Antje‐Christin
Spadea, Maria Francesca
Both, Stefan
author_sort Thummerer, Adrian
collection PubMed
description PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone‐beam CT (CBCT) can provide these daily images, but x‐ray scattering limits CBCT‐image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT‐based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients. METHODS: A dataset of 33 thoracic cancer patients, containing CBCTs, same‐day repeat CTs (rCT), planning‐CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT‐based correction method. Mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU‐accuracy of sCTs in terms of range errors. RESULTS: On average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%). CONCLUSION: CBCT‐based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters.
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spelling pubmed-92991152022-07-21 Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer Thummerer, Adrian Seller Oria, Carmen Zaffino, Paolo Meijers, Arturs Guterres Marmitt, Gabriel Wijsman, Robin Seco, Joao Langendijk, Johannes Albertus Knopf, Antje‐Christin Spadea, Maria Francesca Both, Stefan Med Phys DIAGNOSTIC IMAGING (IONIZING AND NON‐IONIZING) PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone‐beam CT (CBCT) can provide these daily images, but x‐ray scattering limits CBCT‐image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT‐based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients. METHODS: A dataset of 33 thoracic cancer patients, containing CBCTs, same‐day repeat CTs (rCT), planning‐CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT‐based correction method. Mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU‐accuracy of sCTs in terms of range errors. RESULTS: On average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%). CONCLUSION: CBCT‐based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters. John Wiley and Sons Inc. 2021-11-16 2021-12 /pmc/articles/PMC9299115/ /pubmed/34725829 http://dx.doi.org/10.1002/mp.15333 Text en © 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://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 DIAGNOSTIC IMAGING (IONIZING AND NON‐IONIZING)
Thummerer, Adrian
Seller Oria, Carmen
Zaffino, Paolo
Meijers, Arturs
Guterres Marmitt, Gabriel
Wijsman, Robin
Seco, Joao
Langendijk, Johannes Albertus
Knopf, Antje‐Christin
Spadea, Maria Francesca
Both, Stefan
Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer
title Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer
title_full Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer
title_fullStr Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer
title_full_unstemmed Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer
title_short Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer
title_sort clinical suitability of deep learning based synthetic cts for adaptive proton therapy of lung cancer
topic DIAGNOSTIC IMAGING (IONIZING AND NON‐IONIZING)
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299115/
https://www.ncbi.nlm.nih.gov/pubmed/34725829
http://dx.doi.org/10.1002/mp.15333
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