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HPV Detection Methods: Towards Personalized Prevention

HPV Detection Methods: Towards Personalized Prevention

Aris Spathis, Christine Kottaridi, Abraham Pouliakis, Stavros Archondakis, Petros Karakitsos
Copyright: © 2017 |Pages: 35
ISBN13: 9781522505495|ISBN10: 1522505490|EISBN13: 9781522506386
DOI: 10.4018/978-1-5225-0549-5.ch008
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MLA

Spathis, Aris, et al. "HPV Detection Methods: Towards Personalized Prevention." Oncology: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2017, pp. 267-301. https://doi.org/10.4018/978-1-5225-0549-5.ch008

APA

Spathis, A., Kottaridi, C., Pouliakis, A., Archondakis, S., & Karakitsos, P. (2017). HPV Detection Methods: Towards Personalized Prevention. In I. Management Association (Ed.), Oncology: Breakthroughs in Research and Practice (pp. 267-301). IGI Global. https://doi.org/10.4018/978-1-5225-0549-5.ch008

Chicago

Spathis, Aris, et al. "HPV Detection Methods: Towards Personalized Prevention." In Oncology: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 267-301. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0549-5.ch008

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Abstract

Human papilloma viruses (HPVs) have been acknowledged to be the leading risk factor of cervical intra-epithelial lesion creation (CIN) and cervical cancer development (CxCa). Many different techniques have been created and utilized in HPV detection and monitoring with a vast amount of them being commercialized and few of them integrated in official screening strategies. A growing trend for DNA typing of the 14 most commonly accepted high risk HPV types has been introduced, supporting that in many cases molecular testing could replace classic morphologic diagnostic routines, even though DNA detection has lower specificity than other molecular and morphology tests. However, there have been limited attempts in combining data from all different techniques to provide efficient patient triaging schemes, since, apart from the obvious increase of patient cost, the amount of data and its interpretation in patient management has been impossible. Complex computer based clinical support decision systems, many of which are based on artificial intelligence may abolish these limitations.

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