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Raman microspectroscopy as a biomarking tool for in vitro diagnosis of cancer: a feasibility study
AIM: To elucidate whether Raman spectroscopy aided by extensive spectral database and neural network analysis can be a fast and confident biomarking tool for the diagnosis of various types of cancer. METHODS: Study included 27 patients with 11 different malignant tumors. Using Raman microscopy (RM)...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Croatian Medical Schools
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3541581/ https://www.ncbi.nlm.nih.gov/pubmed/23275320 http://dx.doi.org/10.3325/cmj.2012.53.551 |
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author | Pavićević, Aleksandra Glumac, Sofija Sopta, Jelena Popović-Bijelić, Ana Mojović, Miloš Bačić, Goran |
author_facet | Pavićević, Aleksandra Glumac, Sofija Sopta, Jelena Popović-Bijelić, Ana Mojović, Miloš Bačić, Goran |
author_sort | Pavićević, Aleksandra |
collection | PubMed |
description | AIM: To elucidate whether Raman spectroscopy aided by extensive spectral database and neural network analysis can be a fast and confident biomarking tool for the diagnosis of various types of cancer. METHODS: Study included 27 patients with 11 different malignant tumors. Using Raman microscopy (RM) a total of 540 Raman spectra were recorded from histology specimens of both tumors and surrounding healthy tissues. Spectra were analyzed using the principal component analysis (PCA) and results, along with histopathology data, were used to train the neural network (NN) learning algorithm. Independent sets of spectra were used to test the accuracy of PCA/NN tissue classification. RESULTS: The confident tumor identification for the purpose of medical diagnosis has to be performed by taking into account the whole spectral shape, and not only particular spectral bands. The use of PCA/NN analysis showed overall sensitivity of 96% with 4% false negative tumor classification. The specificity of distinguishing tumor types was 80%. These results are comparable to previously published data where tumors of only one tissue type were examined and can be regarded satisfactorily for a relatively small database of Raman spectra used here. CONCLUSION: In vitro RM combined with PCA/NN is an almost fully automated method for histopathology at the level of macromolecules. Supported by an extensive tumor spectra database, it could become a customary histological analysis tool for fast and reliable diagnosis of different types of cancer in clinical settings. |
format | Online Article Text |
id | pubmed-3541581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Croatian Medical Schools |
record_format | MEDLINE/PubMed |
spelling | pubmed-35415812013-01-17 Raman microspectroscopy as a biomarking tool for in vitro diagnosis of cancer: a feasibility study Pavićević, Aleksandra Glumac, Sofija Sopta, Jelena Popović-Bijelić, Ana Mojović, Miloš Bačić, Goran Croat Med J Medical Research in Biophysics AIM: To elucidate whether Raman spectroscopy aided by extensive spectral database and neural network analysis can be a fast and confident biomarking tool for the diagnosis of various types of cancer. METHODS: Study included 27 patients with 11 different malignant tumors. Using Raman microscopy (RM) a total of 540 Raman spectra were recorded from histology specimens of both tumors and surrounding healthy tissues. Spectra were analyzed using the principal component analysis (PCA) and results, along with histopathology data, were used to train the neural network (NN) learning algorithm. Independent sets of spectra were used to test the accuracy of PCA/NN tissue classification. RESULTS: The confident tumor identification for the purpose of medical diagnosis has to be performed by taking into account the whole spectral shape, and not only particular spectral bands. The use of PCA/NN analysis showed overall sensitivity of 96% with 4% false negative tumor classification. The specificity of distinguishing tumor types was 80%. These results are comparable to previously published data where tumors of only one tissue type were examined and can be regarded satisfactorily for a relatively small database of Raman spectra used here. CONCLUSION: In vitro RM combined with PCA/NN is an almost fully automated method for histopathology at the level of macromolecules. Supported by an extensive tumor spectra database, it could become a customary histological analysis tool for fast and reliable diagnosis of different types of cancer in clinical settings. Croatian Medical Schools 2012-12 /pmc/articles/PMC3541581/ /pubmed/23275320 http://dx.doi.org/10.3325/cmj.2012.53.551 Text en Copyright © 2012 by the Croatian Medical Journal. All rights reserved. http://creativecommons.org/licenses/by/2.5/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Medical Research in Biophysics Pavićević, Aleksandra Glumac, Sofija Sopta, Jelena Popović-Bijelić, Ana Mojović, Miloš Bačić, Goran Raman microspectroscopy as a biomarking tool for in vitro diagnosis of cancer: a feasibility study |
title | Raman microspectroscopy as a biomarking tool for in vitro diagnosis of cancer: a feasibility study |
title_full | Raman microspectroscopy as a biomarking tool for in vitro diagnosis of cancer: a feasibility study |
title_fullStr | Raman microspectroscopy as a biomarking tool for in vitro diagnosis of cancer: a feasibility study |
title_full_unstemmed | Raman microspectroscopy as a biomarking tool for in vitro diagnosis of cancer: a feasibility study |
title_short | Raman microspectroscopy as a biomarking tool for in vitro diagnosis of cancer: a feasibility study |
title_sort | raman microspectroscopy as a biomarking tool for in vitro diagnosis of cancer: a feasibility study |
topic | Medical Research in Biophysics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3541581/ https://www.ncbi.nlm.nih.gov/pubmed/23275320 http://dx.doi.org/10.3325/cmj.2012.53.551 |
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