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Date
2023-06-08Author
Sarpe, CristianCiobotea, Elena RamelaMorscher, Christoph BurghardZielinski, BastianBraun, HendrikeSenftleben, ArneRüschoff, JosefBaumert, ThomasSubject
530 Physics BrustkrebsLaserinduzierte Breakdown-SpektroskopieMaschinelles LernenFemtosekundenlaserMetadata
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Aufsatz
Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning
Abstract
In the treatment of most newly discovered solid cancerous tumors, surgery remains the first treatment option. An important factor in the success of these operations is the precise identification of oncological safety margins to ensure the complete removal of the tumor without affecting much of the neighboring healthy tissue. Here we report on the possibility of applying femtosecond Laser-Induced Breakdown Spectroscopy (LIBS) combined with Machine Learning algorithms as an alternative discrimination technique to differentiate cancerous tissue. The emission spectra following the ablation on thin fixed liver and breast postoperative samples were recorded with high spatial resolution; adjacent stained sections served as a reference for tissue identification by classical pathological analysis. In a proof of principle test performed on liver tissue, Artificial Neural Networks and Random Forest algorithms were able to differentiate both healthy and tumor tissue with a very high Classification Accuracy of around 0.95. The ability to identify unknown tissue was performed on breast samples from different patients, also providing a high level of discrimination. Our results show that LIBS with femtosecond lasers is a technique with potential to be used in clinical applications for rapid identification of tissue type in the intraoperative surgical field.
Citation
In: Scientific Reports Volume 13 (2023-06-08) eissn:2045-2322Sponsorship
Gefördert durch den Publikationsfonds der Universität KasselCitation
@article{doi:10.17170/kobra-202311179035,
author={Sarpe, Cristian and Ciobotea, Elena Ramela and Morscher, Christoph Burghard and Zielinski, Bastian and Braun, Hendrike and Senftleben, Arne and Rüschoff, Josef and Baumert, Thomas},
title={Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning},
journal={Scientific Reports},
year={2023}
}
0500 Oax 0501 Text $btxt$2rdacontent 0502 Computermedien $bc$2rdacarrier 1100 2023$n2023 1500 1/eng 2050 ##0##http://hdl.handle.net/123456789/15194 3000 Sarpe, Cristian 3010 Ciobotea, Elena Ramela 3010 Morscher, Christoph Burghard 3010 Zielinski, Bastian 3010 Braun, Hendrike 3010 Senftleben, Arne 3010 Rüschoff, Josef 3010 Baumert, Thomas 4000 Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning / Sarpe, Cristian 4030 4060 Online-Ressource 4085 ##0##=u http://nbn-resolving.de/http://hdl.handle.net/123456789/15194=x R 4204 \$dAufsatz 4170 5550 {{Brustkrebs}} 5550 {{Laserinduzierte Breakdown-Spektroskopie}} 5550 {{Maschinelles Lernen}} 5550 {{Femtosekundenlaser}} 7136 ##0##http://hdl.handle.net/123456789/15194
2023-11-17T14:30:19Z 2023-11-17T14:30:19Z 2023-06-08 doi:10.17170/kobra-202311179035 http://hdl.handle.net/123456789/15194 Gefördert durch den Publikationsfonds der Universität Kassel eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ Breast cancer Cancer Optical spectroscopy Ultrafast lasers 530 Identification of tumor tissue in thin pathological samples via femtosecond laser-induced breakdown spectroscopy and machine learning Aufsatz In the treatment of most newly discovered solid cancerous tumors, surgery remains the first treatment option. An important factor in the success of these operations is the precise identification of oncological safety margins to ensure the complete removal of the tumor without affecting much of the neighboring healthy tissue. Here we report on the possibility of applying femtosecond Laser-Induced Breakdown Spectroscopy (LIBS) combined with Machine Learning algorithms as an alternative discrimination technique to differentiate cancerous tissue. The emission spectra following the ablation on thin fixed liver and breast postoperative samples were recorded with high spatial resolution; adjacent stained sections served as a reference for tissue identification by classical pathological analysis. In a proof of principle test performed on liver tissue, Artificial Neural Networks and Random Forest algorithms were able to differentiate both healthy and tumor tissue with a very high Classification Accuracy of around 0.95. The ability to identify unknown tissue was performed on breast samples from different patients, also providing a high level of discrimination. Our results show that LIBS with femtosecond lasers is a technique with potential to be used in clinical applications for rapid identification of tissue type in the intraoperative surgical field. open access Sarpe, Cristian Ciobotea, Elena Ramela Morscher, Christoph Burghard Zielinski, Bastian Braun, Hendrike Senftleben, Arne Rüschoff, Josef Baumert, Thomas 10 Seiten doi:10.1038/s41598-023-36155-8 Brustkrebs Laserinduzierte Breakdown-Spektroskopie Maschinelles Lernen Femtosekundenlaser publishedVersion eissn:2045-2322 Scientific Reports Volume 13 false 9250
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