Cargando…

Clinical Evaluation of an Auto-Segmentation Tool for Spine SBRT Treatment

PURPOSE: Spine SBRT target delineation is time-consuming due to the complex bone structure. Recently, Elements SmartBrush Spine (ESS) was developed by Brainlab to automatically generate a clinical target volume (CTV) based on gross tumor volume (GTV). The aim of this project is to evaluate the accur...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Yingxuan, Vinogradskiy, Yevgeniy, Yu, Yan, Shi, Wenyin, Liu, Haisong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963426/
https://www.ncbi.nlm.nih.gov/pubmed/35359361
http://dx.doi.org/10.3389/fonc.2022.842579
_version_ 1784677988665655296
author Chen, Yingxuan
Vinogradskiy, Yevgeniy
Yu, Yan
Shi, Wenyin
Liu, Haisong
author_facet Chen, Yingxuan
Vinogradskiy, Yevgeniy
Yu, Yan
Shi, Wenyin
Liu, Haisong
author_sort Chen, Yingxuan
collection PubMed
description PURPOSE: Spine SBRT target delineation is time-consuming due to the complex bone structure. Recently, Elements SmartBrush Spine (ESS) was developed by Brainlab to automatically generate a clinical target volume (CTV) based on gross tumor volume (GTV). The aim of this project is to evaluate the accuracy and efficiency of ESS auto-segmentation. METHODS: Twenty spine SBRT patients with 21 target sites treated at our institution were used for this retrospective comparison study. Planning CT/MRI images and physician-drawn GTVs were inputs for ESS. ESS can automatically segment the vertebra, split the vertebra into 6 sectors, and generate a CTV based on the GTV location, according to the International Spine Radiosurgery Consortium (ISRC) Consensus guidelines. The auto-segmented CTV can be edited by including/excluding sectors of the vertebra, if necessary. The ESS-generated CTV contour was then compared to the clinically used CTV using qualitative and quantitative methods. The CTV contours were compared using visual assessment by the clinicians, relative volume differences (RVD), distance of center of mass (DCM), and three other common contour similarity measurements such as dice similarity coefficient (DICE), Hausdorff distance (HD), and 95% Hausdorff distance (HD95). RESULTS: Qualitatively, the study showed that ESS can segment vertebra more accurately and consistently than humans at normal curvature conditions. The accuracy of CTV delineation can be improved significantly if the auto-segmentation is used as the first step. Conversely, ESS may mistakenly split or join different vertebrae when large curvatures in anatomy exist. In this study, human interactions were needed in 7 of 21 cases to generate the final CTVs by including/excluding sectors of the vertebra. In 90% of cases, the RVD were within ±15%. The RVD, DCM, DICE, HD, and HD95 for the 21 cases were 3% ± 12%, 1.9 ± 1.5 mm, 0.86 ± 0.06, 13.34 ± 7.47 mm, and 4.67 ± 2.21 mm, respectively. CONCLUSION: ESS can auto-segment a CTV quickly and accurately and has a good agreement with clinically used CTV. Inter-person variation and contouring time can be reduced with ESS. Physician editing is needed for some occasions. Our study supports the idea of using ESS as the first step for spine SBRT target delineation to improve the contouring consistency as well as to reduce the contouring time.
format Online
Article
Text
id pubmed-8963426
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-89634262022-03-30 Clinical Evaluation of an Auto-Segmentation Tool for Spine SBRT Treatment Chen, Yingxuan Vinogradskiy, Yevgeniy Yu, Yan Shi, Wenyin Liu, Haisong Front Oncol Oncology PURPOSE: Spine SBRT target delineation is time-consuming due to the complex bone structure. Recently, Elements SmartBrush Spine (ESS) was developed by Brainlab to automatically generate a clinical target volume (CTV) based on gross tumor volume (GTV). The aim of this project is to evaluate the accuracy and efficiency of ESS auto-segmentation. METHODS: Twenty spine SBRT patients with 21 target sites treated at our institution were used for this retrospective comparison study. Planning CT/MRI images and physician-drawn GTVs were inputs for ESS. ESS can automatically segment the vertebra, split the vertebra into 6 sectors, and generate a CTV based on the GTV location, according to the International Spine Radiosurgery Consortium (ISRC) Consensus guidelines. The auto-segmented CTV can be edited by including/excluding sectors of the vertebra, if necessary. The ESS-generated CTV contour was then compared to the clinically used CTV using qualitative and quantitative methods. The CTV contours were compared using visual assessment by the clinicians, relative volume differences (RVD), distance of center of mass (DCM), and three other common contour similarity measurements such as dice similarity coefficient (DICE), Hausdorff distance (HD), and 95% Hausdorff distance (HD95). RESULTS: Qualitatively, the study showed that ESS can segment vertebra more accurately and consistently than humans at normal curvature conditions. The accuracy of CTV delineation can be improved significantly if the auto-segmentation is used as the first step. Conversely, ESS may mistakenly split or join different vertebrae when large curvatures in anatomy exist. In this study, human interactions were needed in 7 of 21 cases to generate the final CTVs by including/excluding sectors of the vertebra. In 90% of cases, the RVD were within ±15%. The RVD, DCM, DICE, HD, and HD95 for the 21 cases were 3% ± 12%, 1.9 ± 1.5 mm, 0.86 ± 0.06, 13.34 ± 7.47 mm, and 4.67 ± 2.21 mm, respectively. CONCLUSION: ESS can auto-segment a CTV quickly and accurately and has a good agreement with clinically used CTV. Inter-person variation and contouring time can be reduced with ESS. Physician editing is needed for some occasions. Our study supports the idea of using ESS as the first step for spine SBRT target delineation to improve the contouring consistency as well as to reduce the contouring time. Frontiers Media S.A. 2022-03-14 /pmc/articles/PMC8963426/ /pubmed/35359361 http://dx.doi.org/10.3389/fonc.2022.842579 Text en Copyright © 2022 Chen, Vinogradskiy, Yu, Shi and Liu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Chen, Yingxuan
Vinogradskiy, Yevgeniy
Yu, Yan
Shi, Wenyin
Liu, Haisong
Clinical Evaluation of an Auto-Segmentation Tool for Spine SBRT Treatment
title Clinical Evaluation of an Auto-Segmentation Tool for Spine SBRT Treatment
title_full Clinical Evaluation of an Auto-Segmentation Tool for Spine SBRT Treatment
title_fullStr Clinical Evaluation of an Auto-Segmentation Tool for Spine SBRT Treatment
title_full_unstemmed Clinical Evaluation of an Auto-Segmentation Tool for Spine SBRT Treatment
title_short Clinical Evaluation of an Auto-Segmentation Tool for Spine SBRT Treatment
title_sort clinical evaluation of an auto-segmentation tool for spine sbrt treatment
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963426/
https://www.ncbi.nlm.nih.gov/pubmed/35359361
http://dx.doi.org/10.3389/fonc.2022.842579
work_keys_str_mv AT chenyingxuan clinicalevaluationofanautosegmentationtoolforspinesbrttreatment
AT vinogradskiyyevgeniy clinicalevaluationofanautosegmentationtoolforspinesbrttreatment
AT yuyan clinicalevaluationofanautosegmentationtoolforspinesbrttreatment
AT shiwenyin clinicalevaluationofanautosegmentationtoolforspinesbrttreatment
AT liuhaisong clinicalevaluationofanautosegmentationtoolforspinesbrttreatment