Artificial intelligence estimates the impact of human papillomavirus types in influencing the risk of cervical dysplasia recurrence: progress toward a more personalized approach

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

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