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Raman spectroscopy and topological machine learning for cancer grading
In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160071/ https://www.ncbi.nlm.nih.gov/pubmed/37142690 http://dx.doi.org/10.1038/s41598-023-34457-5 |
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author | Conti, Francesco D’Acunto, Mario Caudai, Claudia Colantonio, Sara Gaeta, Raffaele Moroni, Davide Pascali, Maria Antonietta |
author_facet | Conti, Francesco D’Acunto, Mario Caudai, Claudia Colantonio, Sara Gaeta, Raffaele Moroni, Davide Pascali, Maria Antonietta |
author_sort | Conti, Francesco |
collection | PubMed |
description | In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system. |
format | Online Article Text |
id | pubmed-10160071 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101600712023-05-06 Raman spectroscopy and topological machine learning for cancer grading Conti, Francesco D’Acunto, Mario Caudai, Claudia Colantonio, Sara Gaeta, Raffaele Moroni, Davide Pascali, Maria Antonietta Sci Rep Article In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system. Nature Publishing Group UK 2023-05-04 /pmc/articles/PMC10160071/ /pubmed/37142690 http://dx.doi.org/10.1038/s41598-023-34457-5 Text en © The Author(s) 2023 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 | Article Conti, Francesco D’Acunto, Mario Caudai, Claudia Colantonio, Sara Gaeta, Raffaele Moroni, Davide Pascali, Maria Antonietta Raman spectroscopy and topological machine learning for cancer grading |
title | Raman spectroscopy and topological machine learning for cancer grading |
title_full | Raman spectroscopy and topological machine learning for cancer grading |
title_fullStr | Raman spectroscopy and topological machine learning for cancer grading |
title_full_unstemmed | Raman spectroscopy and topological machine learning for cancer grading |
title_short | Raman spectroscopy and topological machine learning for cancer grading |
title_sort | raman spectroscopy and topological machine learning for cancer grading |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160071/ https://www.ncbi.nlm.nih.gov/pubmed/37142690 http://dx.doi.org/10.1038/s41598-023-34457-5 |
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