An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

• Published in: BioMed research international Vol. 2014; no. 2014; pp. 1 - 20
• Main Authors: Bountris, Panagiotis, Haritou, Maria, Pouliakis, Abraham, Margari, Niki, Kyrgiou, Maria, Spathis, Aris, Pappas, Asimakis, Panayiotides, Ioannis, Paraskevaidis, Evangelos A., Karakitsos, Petros, Koutsouris, Dimitrios-Dionyssios
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Puplishing Corporation 01.01.2014; Hindawi Publishing Corporation; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial intelligence; Atypical Squamous Cells of the Cervix; Biomarkers; Cellular biology; Cervical cancer; Computer engineering; Data processing; Databases as Topic; Decision Support Systems, Clinical; Deoxyribonucleic acid; Disease; DNA; Family medical history; Female; Genetic testing; Human papillomavirus; Humans; Infections; Laboratories; Medicine; Methods; Neural networks; Neural Networks, Computer; Obstetrics; Precision Medicine; Probability; Reproducibility of Results; Studies; Uterine Cervical Dysplasia - diagnosis; Uterine Cervical Dysplasia - pathology
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2014/341483

Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.