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Journal of Biomedicine and Biotechnology
Volume 2012 (2012), Article ID 303192, 8 pages
http://dx.doi.org/10.1155/2012/303192
Research Article

Identification of Women for Referral to Colposcopy by Neural Networks: A Preliminary Study Based on LBC and Molecular Biomarkers

1Department of Cytopathology, Medical School Attikon University Hospital, University of Athens, 12462 Athens, Greece
23rd Department of Obstetrics and Gynecology, Medical School Attikon University Hospital, University of Athens, 12462 Athens, Greece
3Department of Obstetrics and Gynecology, University Hospital of Ioannina, 45500 Ioannina, Greece
4West London Gynaecological Cancer Center, Queen Charlotte's and Chelsea, Hammersmith Hospital, Department of Obstetrics and Gynaecology, Imperial Healthcare NHS Trust Division of Surgery and Cancer, Faculty of Medicine, Imperial College, London W12 0HS, UK
52nd Department of Pathology, Medical School Attikon University Hospital, University of Athens, 12462 Athens, Greece

Received 5 May 2012; Accepted 30 May 2012

Academic Editor: P. J. Oefner

Copyright © 2012 Petros Karakitsos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective of this study is to investigate the potential of the learning vector quantizer neural network (LVQ-NN) classifier on various diagnostic variables used in the modern cytopathology laboratory and to build an algorithm that may facilitate the classification of individual cases. From all women included in the study, a liquid-based cytology sample was obtained; this was tested via HPV DNA test, E6/E7 HPV mRNA test, and p16 immunostaining. The data were classified by the LVQ-NN into two groups: CIN-2 or worse and CIN-1 or less. Half of the cases were used to train the LVQ-NN; the remaining cases (test set) were used for validation. Out of the 1258 cases, cytology identified correctly 72.90% of the CIN-2 or worst cases and 97.37% of the CIN-1 or less cases, with overall accuracy 94.36%. The application of the LVQ-NN on the test set allowed correct classification for 84.62% of the cases with CIN-2 or worse and 97.64% of the cases with CIN-1 or less, with overall accuracy of 96.03%. The use of the LVQ-NN with cytology and the proposed biomarkers improves significantly the correct classification of cervical precancerous lesions and/or cancer and may facilitate diagnosis and patient management.