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
Department of Gynecologic Oncology.
Department of Obstetrics and Gynecology, IRCCS AOU San Martino.
Eur J Cancer Prev. 2019 Mar;28(2):81-86. doi: 10.1097/CEJ.0000000000000432.
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.
本研究的目的是确定人乳头瘤病毒（HPV）基因型预处理是否可预测宫颈发育异常持续/复发的风险。对连续5104名接受HPV-DNA检测的女性的前瞻性收集数据进行回顾性分析，并与接受生殖器HPV相关感染随访或药物/手术治疗的女性的回顾性数据进行匹配。为了权衡不同HPV基因型在预测宫颈发育异常持续/复发中的重要性，使用了人工神经网络（ANN）分析。ANN从结构和功能的角度模拟生物神经元系统：与神经元一样，ANN通过学习阶段过程获取知识，并允许权衡协变量的重要性，从而确定变量对多因素现象的影响程度。总体而言，对5104名女性进行了HPV检测。其中，1273名（25%）患者接受了HPV相关疾病的治疗。分别有807名（59%）和386名（30%）患者接受了激光锥切术和宫颈汽化术，45名（5.5%）患者接受了二次宫颈锥切术。ANN技术表明，预测宫颈发育异常持续/复发的最重要基因型为HPV-16（标准化重要性：100%）、HPV-59（标准化重要性：51.2%）、HPV-52（标准化重要性：47.7%）、HPV-18（标准化重要性：32.8%）和HPV-45（标准化重要性：30.2%）。除HPV-45外，所有这些基因型的预处理诊断均与宫颈发育异常持续/复发风险增加相关；预处理诊断也通过标准单变量和多变量模型得出（P<0.01）。HPV-16、HPV-18、HPV-52和HPV-59的预处理阳性可能与治疗后宫颈发育异常持续/复发风险增加相关。这些数据可能有助于患者咨询，并实施新的疫苗接种计划。
