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Automated elaborate resection planning for bone tumor surgery

PURPOSE: Planning for bone tumor resection surgery is a technically demanding and time-consuming task, reliant on manual positioning of planar cuts in a virtual space. More elaborate cutting approaches may be possible through the use of surgical robots or patient-specific instruments; however, metho...

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Autores principales: Hill, Dave, Williamson, Tom, Lai, Chow Yin, Leary, Martin, Brandt, Milan, Choong, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939503/
https://www.ncbi.nlm.nih.gov/pubmed/36319922
http://dx.doi.org/10.1007/s11548-022-02763-4
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author Hill, Dave
Williamson, Tom
Lai, Chow Yin
Leary, Martin
Brandt, Milan
Choong, Peter
author_facet Hill, Dave
Williamson, Tom
Lai, Chow Yin
Leary, Martin
Brandt, Milan
Choong, Peter
author_sort Hill, Dave
collection PubMed
description PURPOSE: Planning for bone tumor resection surgery is a technically demanding and time-consuming task, reliant on manual positioning of planar cuts in a virtual space. More elaborate cutting approaches may be possible through the use of surgical robots or patient-specific instruments; however, methods for preparing such a resection plan must be developed. METHODS: This work describes an automated approach for generating conformal bone tumor resection plans, where the resection geometry is defined by the convex hull of the tumor, and a focal point. The resection geometry is optimized using particle swarm, where the volume of healthy bone collaterally resected with the tumor is minimized. The approach was compared to manually prepared planar resection plans from an experienced surgeon for 20 tumor cases. RESULTS: It was found that algorithm-generated hull-type resections greatly reduced the volume of collaterally resected healthy bone. The hull-type resections resulted in statistically significant improvements compared to the manual approach (paired t test, p < 0.001). CONCLUSIONS: The described approach has potential to improve patient outcomes by reducing the volume of healthy bone collaterally resected with the tumor and preserving nearby critical anatomy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11548-022-02763-4.
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spelling pubmed-99395032023-02-21 Automated elaborate resection planning for bone tumor surgery Hill, Dave Williamson, Tom Lai, Chow Yin Leary, Martin Brandt, Milan Choong, Peter Int J Comput Assist Radiol Surg Original Article PURPOSE: Planning for bone tumor resection surgery is a technically demanding and time-consuming task, reliant on manual positioning of planar cuts in a virtual space. More elaborate cutting approaches may be possible through the use of surgical robots or patient-specific instruments; however, methods for preparing such a resection plan must be developed. METHODS: This work describes an automated approach for generating conformal bone tumor resection plans, where the resection geometry is defined by the convex hull of the tumor, and a focal point. The resection geometry is optimized using particle swarm, where the volume of healthy bone collaterally resected with the tumor is minimized. The approach was compared to manually prepared planar resection plans from an experienced surgeon for 20 tumor cases. RESULTS: It was found that algorithm-generated hull-type resections greatly reduced the volume of collaterally resected healthy bone. The hull-type resections resulted in statistically significant improvements compared to the manual approach (paired t test, p < 0.001). CONCLUSIONS: The described approach has potential to improve patient outcomes by reducing the volume of healthy bone collaterally resected with the tumor and preserving nearby critical anatomy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11548-022-02763-4. Springer International Publishing 2022-11-01 2023 /pmc/articles/PMC9939503/ /pubmed/36319922 http://dx.doi.org/10.1007/s11548-022-02763-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Hill, Dave
Williamson, Tom
Lai, Chow Yin
Leary, Martin
Brandt, Milan
Choong, Peter
Automated elaborate resection planning for bone tumor surgery
title Automated elaborate resection planning for bone tumor surgery
title_full Automated elaborate resection planning for bone tumor surgery
title_fullStr Automated elaborate resection planning for bone tumor surgery
title_full_unstemmed Automated elaborate resection planning for bone tumor surgery
title_short Automated elaborate resection planning for bone tumor surgery
title_sort automated elaborate resection planning for bone tumor surgery
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939503/
https://www.ncbi.nlm.nih.gov/pubmed/36319922
http://dx.doi.org/10.1007/s11548-022-02763-4
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