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Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions
INTRODUCTION: Stereotactic body radiotherapy (SBRT) is an ablative method for lung malignancies. Here, the definition of the gross target volume (GTV) is subject to interobserver variation. In this study, we aimed to evaluate the interobserver variability during SBRT and its dosimetric impact, as we...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122930/ https://www.ncbi.nlm.nih.gov/pubmed/36424343 http://dx.doi.org/10.1002/jmrs.637 |
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author | Mohamed, Ahmed Allam Risse, Kathrin Schmitz, Laura Schlenter, Marsha Chughtai, Ahmed Ivanciu, Maria Eble, Michael J. |
author_facet | Mohamed, Ahmed Allam Risse, Kathrin Schmitz, Laura Schlenter, Marsha Chughtai, Ahmed Ivanciu, Maria Eble, Michael J. |
author_sort | Mohamed, Ahmed Allam |
collection | PubMed |
description | INTRODUCTION: Stereotactic body radiotherapy (SBRT) is an ablative method for lung malignancies. Here, the definition of the gross target volume (GTV) is subject to interobserver variation. In this study, we aimed to evaluate the interobserver variability during SBRT and its dosimetric impact, as well as to introduce a semi‐automated delineation tool for both planning computer tomography (P‐CT) and cone beam CT (CBCT) to help to standardise GTV delineation and adaptive volume‐change registration. METHODS: The interobserver variation of GTV manual contours from five physicians was analysed in 15 patients after lung SBRT on free breathing (FB) P‐CT (n = 15) and CBCT (n = 90) before and after each fraction. The dosimetric impact from interobserver variations of GTV based on the original treatment plan was analysed. Next, the accuracy of an in‐house easy‐to‐use semi‐automated‐segmentation algorithm for pulmonary lesions was compared with gold standard contours in FB P‐CT and CBCT, as well as 4D P‐CT of additional 10 patients. RESULTS: The interobserver variability in manual contours resulted in violations of dose coverage of the planning target volume (PTV), which, in turn, resulted in compromised tumour control probability in contours from four physicians. The validation of the semi‐automated delineation algorithm using thorax phantom led to a highly reliable accuracy in defining GTVs. Comparing the unsupervised auto‐contours with the gold standard delineation revealed high equal high concordance for FB P‐CT, 4D P‐CT and CBCT, with a DSC of 0.83, 0.76 and 0.8, respectively. The supervised use of the semi‐automated delineation tool improved its accuracy, with DSCs of 0.86, 0.86 and 0.8 for FB P‐CT, 4D P‐CT and CBCT, respectively. The use of the algorithm was associated with a significantly shorter working time. The semi‐automated delineation tool can accurately register volume changes in CBCTs. CONCLUSION: The segmentation algorithm provides a reliable, standardised and time‐saving alternative for manual delineation in lung SBRT in P‐CT and CBCT. |
format | Online Article Text |
id | pubmed-10122930 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101229302023-04-24 Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions Mohamed, Ahmed Allam Risse, Kathrin Schmitz, Laura Schlenter, Marsha Chughtai, Ahmed Ivanciu, Maria Eble, Michael J. J Med Radiat Sci Original Articles INTRODUCTION: Stereotactic body radiotherapy (SBRT) is an ablative method for lung malignancies. Here, the definition of the gross target volume (GTV) is subject to interobserver variation. In this study, we aimed to evaluate the interobserver variability during SBRT and its dosimetric impact, as well as to introduce a semi‐automated delineation tool for both planning computer tomography (P‐CT) and cone beam CT (CBCT) to help to standardise GTV delineation and adaptive volume‐change registration. METHODS: The interobserver variation of GTV manual contours from five physicians was analysed in 15 patients after lung SBRT on free breathing (FB) P‐CT (n = 15) and CBCT (n = 90) before and after each fraction. The dosimetric impact from interobserver variations of GTV based on the original treatment plan was analysed. Next, the accuracy of an in‐house easy‐to‐use semi‐automated‐segmentation algorithm for pulmonary lesions was compared with gold standard contours in FB P‐CT and CBCT, as well as 4D P‐CT of additional 10 patients. RESULTS: The interobserver variability in manual contours resulted in violations of dose coverage of the planning target volume (PTV), which, in turn, resulted in compromised tumour control probability in contours from four physicians. The validation of the semi‐automated delineation algorithm using thorax phantom led to a highly reliable accuracy in defining GTVs. Comparing the unsupervised auto‐contours with the gold standard delineation revealed high equal high concordance for FB P‐CT, 4D P‐CT and CBCT, with a DSC of 0.83, 0.76 and 0.8, respectively. The supervised use of the semi‐automated delineation tool improved its accuracy, with DSCs of 0.86, 0.86 and 0.8 for FB P‐CT, 4D P‐CT and CBCT, respectively. The use of the algorithm was associated with a significantly shorter working time. The semi‐automated delineation tool can accurately register volume changes in CBCTs. CONCLUSION: The segmentation algorithm provides a reliable, standardised and time‐saving alternative for manual delineation in lung SBRT in P‐CT and CBCT. John Wiley and Sons Inc. 2022-11-24 2023-04 /pmc/articles/PMC10122930/ /pubmed/36424343 http://dx.doi.org/10.1002/jmrs.637 Text en © 2022 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology. 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 | Original Articles Mohamed, Ahmed Allam Risse, Kathrin Schmitz, Laura Schlenter, Marsha Chughtai, Ahmed Ivanciu, Maria Eble, Michael J. Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions |
title | Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions |
title_full | Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions |
title_fullStr | Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions |
title_full_unstemmed | Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions |
title_short | Clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning CT and CBCT in stereotactic body radiotherapy (SBRT) for peripheral lung lesions |
title_sort | clinical validation of a semi‐automated segmentation algorithm for target volume definition on planning ct and cbct in stereotactic body radiotherapy (sbrt) for peripheral lung lesions |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122930/ https://www.ncbi.nlm.nih.gov/pubmed/36424343 http://dx.doi.org/10.1002/jmrs.637 |
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