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

• Published in: BioMed research international Vol. 2012; no. 2012; pp. 1 - 8
• Main Authors: Chranioti, Aikaterini, Kottaridi, Christine, Valasoulis, George, Panayiotides, Ioannis, Paraskevaidis, Evangelos A., Karakitsos, Petros, Meristoudis, Christos, Kyrgiou, Maria, Spathis, Aris, Chrelias, Charalampos, Pouliakis, Abraham, Koliopoulos, George
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Puplishing Corporation 01.01.2012; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: Accuracy; Biomarkers; Biomarkers - blood; Biomarkers, Tumor - blood; Cervical cancer; Classification; Colposcopy; Deoxyribonucleic acid; Diagnosis, Computer-Assisted - methods; Discriminant analysis; DNA; Female; Genetic algorithms; Genetic testing; Histology; Hospitals; Human papillomavirus; Humans; Management; Medical appointments and schedules; Methods; Neural networks; Neural Networks (Computer); Patient Selection; Pattern Recognition, Automated - methods; Pilot Projects; Population; Referral and Consultation; Reproducibility of Results; Sensitivity and Specificity; Studies; Support Vector Machine; Technology application; Uterine Cervical Neoplasms - blood; Uterine Cervical Neoplasms - diagnosis; Vaginal Smears; Greece
• ISSN: 1110-7243; 2314-6133; 1110-7251; 1110-7251; 2314-6141
• DOI: 10.1155/2012/303192

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.