Cargando…

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit thi...

Descripción completa

Detalles Bibliográficos
Autores principales: Blake, Nathan, Gaifulina, Riana, Griffin, Lewis D., Bell, Ian M., Thomas, Geraint M. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222091/
https://www.ncbi.nlm.nih.gov/pubmed/35741300
http://dx.doi.org/10.3390/diagnostics12061491
_version_ 1784732787509559296
author Blake, Nathan
Gaifulina, Riana
Griffin, Lewis D.
Bell, Ian M.
Thomas, Geraint M. H.
author_facet Blake, Nathan
Gaifulina, Riana
Griffin, Lewis D.
Bell, Ian M.
Thomas, Geraint M. H.
author_sort Blake, Nathan
collection PubMed
description Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.
format Online
Article
Text
id pubmed-9222091
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-92220912022-06-24 Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature Blake, Nathan Gaifulina, Riana Griffin, Lewis D. Bell, Ian M. Thomas, Geraint M. H. Diagnostics (Basel) Review Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models. MDPI 2022-06-17 /pmc/articles/PMC9222091/ /pubmed/35741300 http://dx.doi.org/10.3390/diagnostics12061491 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Blake, Nathan
Gaifulina, Riana
Griffin, Lewis D.
Bell, Ian M.
Thomas, Geraint M. H.
Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature
title Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature
title_full Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature
title_fullStr Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature
title_full_unstemmed Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature
title_short Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature
title_sort machine learning of raman spectroscopy data for classifying cancers: a review of the recent literature
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222091/
https://www.ncbi.nlm.nih.gov/pubmed/35741300
http://dx.doi.org/10.3390/diagnostics12061491
work_keys_str_mv AT blakenathan machinelearningoframanspectroscopydataforclassifyingcancersareviewoftherecentliterature
AT gaifulinariana machinelearningoframanspectroscopydataforclassifyingcancersareviewoftherecentliterature
AT griffinlewisd machinelearningoframanspectroscopydataforclassifyingcancersareviewoftherecentliterature
AT bellianm machinelearningoframanspectroscopydataforclassifyingcancersareviewoftherecentliterature
AT thomasgeraintmh machinelearningoframanspectroscopydataforclassifyingcancersareviewoftherecentliterature