Analytical and Bioanalytical Chemistry

, Volume 398, Issue 5, pp 2191–2201

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

  • Jemma G. Kelly
  • Plamen P. Angelov
  • Júlio Trevisan
  • Anastasia Vlachopoulou
  • Evangelos Paraskevaidis
  • Pierre L. Martin-Hirsch
  • Francis L. Martin
Original Paper

DOI: 10.1007/s00216-010-4179-5

Cite this article as:
Kelly, J.G., Angelov, P.P., Trevisan, J. et al. Anal Bioanal Chem (2010) 398: 2191. doi:10.1007/s00216-010-4179-5

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. This predictive 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 incrementally to 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). Development of eClass to classify such datasets for applications such as screening exhibits robustness in identifying a dichotomous marker of invasive disease progression.

https://static-content.springer.com/image/art%3A10.1007%2Fs00216-010-4179-5/MediaObjects/216_2010_4179_Figa_HTML.gif
Figure

Mid-IR spectral data of exfoliative cervical cytology may be classified by eClass and facilitate prediction in a clinical setting

Keywords

ATR-FTIR spectroscopyCancer screeningeClassExfoliative cervical cytologyFuzzyPrediction

Abbreviations

ANN

Artificial neural networks

ATR-FTIR spectroscopy

Attenuated total reflection Fourier-transform infrared spectroscopy

CIN

Cervical intraepithelial neoplasia

eClass

Evolving fuzzy classifier

HPV

Human papillomavirus

HSIL

High-grade squamous intraepithelial lesions

IR

Infrared

k-NN

k-Nearest neighbours

PCA-LDA

Principal component analysis–linear discriminant analysis

SV

Support vector

SVM

Support vector machine

νasPO2

Antisymmetric phosphate stretching vibrations

νsPO2

Symmetric phosphate stretching vibrations

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Jemma G. Kelly
    • 1
  • Plamen P. Angelov
    • 1
    • 2
  • Júlio Trevisan
    • 1
    • 2
  • Anastasia Vlachopoulou
    • 4
  • Evangelos Paraskevaidis
    • 4
  • Pierre L. Martin-Hirsch
    • 3
  • Francis L. Martin
    • 1
  1. 1.Centre for Biophotonics, Lancaster Environment CentreLancaster UniversityLancasterUK
  2. 2.Intelligent Systems Research Laboratory, School of Computing and CommunicationsLancaster UniversityLancasterUK
  3. 3.Lancashire Teaching Hospitals NHS TrustPrestonUK
  4. 4.Department of Obstetrics and GynaecologyUniversity Hospital of IoanninaIoanninaGreece