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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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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. |
format | Online Article Text |
id | pubmed-9939503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
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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