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An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection
Joint Authors
Karakitsos, Petros
Margari, Niki
Kyrgiou, Maria
Koutsouris, Dimitrios-Dionyssios
Panayiotides, Ioannis
Paraskevaidis, Evangelos A.
Pappas, Asimakis
Haritou, Maria
Pouliakis, Abraham
Spathis, Aris
Bountris, Panagiotis
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-09
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Bountris, Panagiotis& Haritou, Maria& Pouliakis, Abraham& Margari, Niki& Kyrgiou, Maria& Spathis, Aris…[et al.]. 2014. An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection. BioMed Research International،Vol. 2014, no. 2014, pp.1-20.
https://search.emarefa.net/detail/BIM-464232
Modern Language Association (MLA)
Bountris, Panagiotis…[et al.]. An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection. BioMed Research International No. 2014 (2014), pp.1-20.
https://search.emarefa.net/detail/BIM-464232
American Medical Association (AMA)
Bountris, Panagiotis& Haritou, Maria& Pouliakis, Abraham& Margari, Niki& Kyrgiou, Maria& Spathis, Aris…[et al.]. An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-20.
https://search.emarefa.net/detail/BIM-464232
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-464232