Artificial intelligence estimates the impact of human papillomavirus types in influencing the risk of cervical dysplasia recurrence: progress toward a more personalized approach. Issue 2 (March 2019)

• Record Type: Journal Article
• Authors: Bogani, Giorgio; Ditto, Antonino; Martinelli, Fabio; Signorelli, Mauro; Chiappa, Valentina; Leone Roberti Maggiore, Umberto; Taverna, Francesca; Lombardo, Claudia; Borghi, Chiara; Scaffa, Cono; Lorusso, Domenica; Raspagliesi, Francesco
• Is Part Of: European journal of cancer prevention. Volume 28:Issue 2(2019)
• Publication Date: 2019-03
• Subjects: artificial neuronal network analysis -- cervical dysplasia -- conization -- genotypes -- human papillomavirus; Cancer -- Prevention -- Periodicals; Neoplasms -- etiology -- Periodicals; Neoplasms -- prevention & control -- Periodicals; Cancer -- Prevention; Periodicals; 616.994052
• DOI: 10.1097/CEJ.0000000000000432
• Languages: English
• ISSNs: 0959-8278

Abstract

Abstract : The objective of this study was to determine whether the pretreatment human papillomavirus (HPV) genotype might predict the risk of cervical dysplasia persistence/recurrence. Retrospective analysis of prospectively collected data of consecutive 5104 women who underwent the HPV-DNA test were matched with retrospective data of women undergoing either follow-up or medical/surgical treatment(s) for genital HPV-related infection(s). Artificial neuronal network (ANN) analysis was used in order to weight the importance of different HPV genotypes in predicting cervical dysplasia persistence/recurrence. ANN simulates a biological neuronal system from both the structural and functional points of view: like neurons, ANN acquires knowledge through a learning-phase process and allows weighting the importance of covariates, thus establishing how much a variable influences a multifactor phenomenon. Overall, 5104 women were tested for HPV. Among them, 1273 (25%) patients underwent treatment for HPV-related disorders. LASER conization and cervical vaporization were performed in 807 (59%) and 386 (30%) patients, respectively, and secondary cervical conization in 45 (5.5%). ANN technology showed that the most important genotypes predicting cervical dysplasia persistence/recurrence were HPV-16 (normalized importance: 100%), HPV-59 (normalized importance: 51.2%), HPV-52 (normalized importance: 47.7%), HPV-18 (normalized importance: 32.8%) and HPV-45 (normalized importance: 30.2%). The pretreatment diagnosis of all of those genotypes, except HPV-45, correlated with an increased risk of cervical dysplasia persistence/recurrence; the pretreatment diagnosis was also arrived at using standard univariate and multivariable models ( P <0.01). Pretreatment positivity for HPV-16, HPV-18, HPV-52 and HPV-59 might correlate with an increased risk of cervical dysplasia persistence/recurrence after treatment. These data might be helpful during patients' counseling and to implement new vaccination programs.