Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -