Peñaranda, F.; Naranjo Ornedo, V.; Lloyd, GR.; Kastl, L.; Kemper, B.; Schnekenburger, J.; Nallala, J.... (2018). Discrimination of skin cancer cells using Fourier transform infrared spectroscopy. Computers in Biology and Medicine. 100:50-61. https://doi.org/10.1016/j.compbiomed.2018.06.023
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/133516
Title:
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Discrimination of skin cancer cells using Fourier transform infrared spectroscopy
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Author:
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Peñaranda, Francisco
Naranjo Ornedo, Valeriana
Lloyd, Gavin R.
Kastl, Lena
Kemper, Björn
Schnekenburger, Jürgen
Nallala, Jayakrupakar
Stone, Nick
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UPV Unit:
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Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
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Issued date:
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Abstract:
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[EN] Fourier transform infrared DO spectroscopy is a highly versatile tool for cell and tissue analysis. Modern commercial FTIR microspectroscopes allow the acquisition of good-quality hyperspectral images from cytopathological ...[+]
[EN] Fourier transform infrared DO spectroscopy is a highly versatile tool for cell and tissue analysis. Modern commercial FTIR microspectroscopes allow the acquisition of good-quality hyperspectral images from cytopathological samples within relatively short times. This study aims at assessing the abilities of FTIR spectra to discriminate different types of cultured skin cell lines by different computer analysis technologies. In particular, 22700 single skin cells, belonging to two non-tumoral and two tumoral cell lines, were analysed. These cells were prepared in three different batches that included each cell type. Different spectral preprocessing and classification strategies were considered, including the current standard approaches to reduce Mie scattering artefacts. Special care was taken for the optimisation, training and evaluation of the learning models in order to avoid possible overfitting. Excellent classification performance (balanced accuracy between 0.85 and 0.95) was achieved when the algorithms were trained and tested with the cells from the same batch. When cells from different batches were used for training and testing the balanced accuracy reached values between 0.35 and 0.6, demonstrating the strong influence of sample preparation on the results and comparability of cell FTIR spectra. A deep study of the most optimistic results was performed in order to identify perturbations that influenced the final classification.
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Subjects:
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Machine learning
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Multivariate analysis
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Cancer diagnosis
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Cytopathology
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Fourier transform infrared spectroscopy
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Copyrigths:
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Reserva de todos los derechos
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Source:
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Computers in Biology and Medicine. (issn:
0010-4825
)
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DOI:
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10.1016/j.compbiomed.2018.06.023
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Publisher:
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Elsevier
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Publisher version:
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https://doi.org/10.1016/j.compbiomed.2018.06.023
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Project ID:
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info:eu-repo/grantAgreement/EC/FP7/317803/EU/MId- to NEaR infrared spectroscopy for improVed medical diAgnostics/
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Thanks:
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This research has been supported by the European Commission under the Seventh Framework Programme, Project MINERVA (317803; http://minerva-project.eu/).
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Type:
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Artículo
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