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Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass

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Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass. / Kerns, Jemma; Angelov, Plamen P.; Trevisan, Júlio et al.
In: Analytical and Bioanalytical Chemistry, Vol. 398, No. 5, 11.2010, p. 2191-2201.

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Kerns J, Angelov PP, Trevisan J, Vlachopoulou A, Paraskevaidis E, Martin-Hirsch PL et al. Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass. Analytical and Bioanalytical Chemistry. 2010 Nov;398(5):2191-2201. doi: 10.1007/s00216-010-4179-5

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@article{94046a0c117a49348a3720678e9c3d44,
title = "Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass",
abstract = "Although the UK cervical screening programme has reduced mortality associated with invasive disease, advancement from a high-throughput predictive methodology that is cost-effective and robust could greatly support the current system. We combined analysis by attenuated total reflection Fourier-transform infrared spectroscopy of cervical cytology with self-learning classifier eClass. Thispredictive algorithm can cope with vast amounts of multidimensional data with variable characteristics. Using a characterised dataset [set A: consisting of UK cervical specimens designated as normal (n=60), low-grade (n=60) or high-grade (n=60)] and one further dataset (set B) consisting of n=30 low-grade samples, we set out to determine whether this approach could be robustly predictive.Variously extending the training set consisting of set A with set B data produced good classification rates with three two-class cascade classifiers. However, a single three-class classifier was equally efficient, producing a user-friendly, applicable methodology with improved interpretability (i.e., better classification with only one set of fuzzy rules). As data from set B were added incrementallyto the training set, the model learned and evolved.Additionally, monitoring of results of the set B low-grade specimens (known to be low-grade cervical cytology specimens) provided the opportunity to explore the possibility of distinguishing patients likely to progress towards invasive disease. eClass exhibited a remarkably robust predictive power in a user-friendly fashion (i.e., high throughput, ease of use) compared to other classifiers (k-nearest neighbours, support vector machines, artificial neural networks). Developmentof eClass to classify such datasets for applications such as screening exhibits robustness in identifying a dichotomous marker of invasive disease progression.",
keywords = "Algorithms, Female, Humans, Neoplasm Staging, Predictive Value of Tests, Spectroscopy, Fourier Transform Infrared, Uterine Cervical Neoplasms",
author = "Jemma Kerns and Angelov, {Plamen P.} and J{\'u}lio Trevisan and Anastasia Vlachopoulou and Evangelos Paraskevaidis and Martin-Hirsch, {Pierre L.} and Martin, {Francis L.}",
year = "2010",
month = nov,
doi = "10.1007/s00216-010-4179-5",
language = "English",
volume = "398",
pages = "2191--2201",
journal = "Analytical and Bioanalytical Chemistry",
issn = "1618-2642",
publisher = "Springer Verlag",
number = "5",

}

RIS

TY - JOUR

T1 - Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass

AU - Kerns, Jemma

AU - Angelov, Plamen P.

AU - Trevisan, Júlio

AU - Vlachopoulou, Anastasia

AU - Paraskevaidis, Evangelos

AU - Martin-Hirsch, Pierre L.

AU - Martin, Francis L.

PY - 2010/11

Y1 - 2010/11

N2 - Although the UK cervical screening programme has reduced mortality associated with invasive disease, advancement from a high-throughput predictive methodology that is cost-effective and robust could greatly support the current system. We combined analysis by attenuated total reflection Fourier-transform infrared spectroscopy of cervical cytology with self-learning classifier eClass. Thispredictive algorithm can cope with vast amounts of multidimensional data with variable characteristics. Using a characterised dataset [set A: consisting of UK cervical specimens designated as normal (n=60), low-grade (n=60) or high-grade (n=60)] and one further dataset (set B) consisting of n=30 low-grade samples, we set out to determine whether this approach could be robustly predictive.Variously extending the training set consisting of set A with set B data produced good classification rates with three two-class cascade classifiers. However, a single three-class classifier was equally efficient, producing a user-friendly, applicable methodology with improved interpretability (i.e., better classification with only one set of fuzzy rules). As data from set B were added incrementallyto the training set, the model learned and evolved.Additionally, monitoring of results of the set B low-grade specimens (known to be low-grade cervical cytology specimens) provided the opportunity to explore the possibility of distinguishing patients likely to progress towards invasive disease. eClass exhibited a remarkably robust predictive power in a user-friendly fashion (i.e., high throughput, ease of use) compared to other classifiers (k-nearest neighbours, support vector machines, artificial neural networks). Developmentof eClass to classify such datasets for applications such as screening exhibits robustness in identifying a dichotomous marker of invasive disease progression.

AB - Although the UK cervical screening programme has reduced mortality associated with invasive disease, advancement from a high-throughput predictive methodology that is cost-effective and robust could greatly support the current system. We combined analysis by attenuated total reflection Fourier-transform infrared spectroscopy of cervical cytology with self-learning classifier eClass. Thispredictive algorithm can cope with vast amounts of multidimensional data with variable characteristics. Using a characterised dataset [set A: consisting of UK cervical specimens designated as normal (n=60), low-grade (n=60) or high-grade (n=60)] and one further dataset (set B) consisting of n=30 low-grade samples, we set out to determine whether this approach could be robustly predictive.Variously extending the training set consisting of set A with set B data produced good classification rates with three two-class cascade classifiers. However, a single three-class classifier was equally efficient, producing a user-friendly, applicable methodology with improved interpretability (i.e., better classification with only one set of fuzzy rules). As data from set B were added incrementallyto the training set, the model learned and evolved.Additionally, monitoring of results of the set B low-grade specimens (known to be low-grade cervical cytology specimens) provided the opportunity to explore the possibility of distinguishing patients likely to progress towards invasive disease. eClass exhibited a remarkably robust predictive power in a user-friendly fashion (i.e., high throughput, ease of use) compared to other classifiers (k-nearest neighbours, support vector machines, artificial neural networks). Developmentof eClass to classify such datasets for applications such as screening exhibits robustness in identifying a dichotomous marker of invasive disease progression.

KW - Algorithms

KW - Female

KW - Humans

KW - Neoplasm Staging

KW - Predictive Value of Tests

KW - Spectroscopy, Fourier Transform Infrared

KW - Uterine Cervical Neoplasms

U2 - 10.1007/s00216-010-4179-5

DO - 10.1007/s00216-010-4179-5

M3 - Journal article

C2 - 20857283

VL - 398

SP - 2191

EP - 2201

JO - Analytical and Bioanalytical Chemistry

JF - Analytical and Bioanalytical Chemistry

SN - 1618-2642

IS - 5

ER -