Abstract

BackgroundCytology and HPV genotype screening play an important role in cervical cancer detection. Whether multiple HPV genotyping can predict cytological lesions remains to be further studied.MethodsTwo thousand two hundred twenty-four females were analyzed for cytology and HPV genotypes test. The possibility of predicting cytological lesions by HPV genotypes test was evaluated by multivariate logistic regression and area under the receiver operator characteristic curve (AUC).ResultAbnormal cytological results were found in 479 participants. A total of 688 patients were detected with HPV infection, 619 with HR-HPV infection and 112 with LR-HRV infection. HPV-52 was found to be the most common type among these patients, and a relatively higher risk of cervical lesions was found in HPV positive females. HPV-16, 31, 33 and 58 were found to have significantly higher infection rates in patients with HSIL and higher lesions. The prediction model was developed based on age and HPV-specific genotypes, with the AUC of 0.73 for cytological abnormalities and 0.82 for HSIL and higher lesions.ConclusionHPV-16, 31, 33 and 58 infection are significant risk factors for cervical lesions. Combined HPV genotypes test can effectively predict cytological abnormalities.

Highlights

  • Cervical cancer remains one of the most common cancers affecting women worldwide [1]

  • Can multiple human papillomavirus (HPV) subtypes be used to predict cervical cytology? Our study analyzed the HPV infection based on different cytological results, to find out the specific HPV genotype that is more likely to cause cervical lesions, and probed into the possibility of predicting cervical cytological lesions with HPV genotype combinations

  • All of the women were tested for liquid-based cytology and for the HPV genotype examination

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Summary

Methods

Two thousand two hundred twenty-four females were analyzed for cytology and HPV genotypes test. The possibility of predicting cytological lesions by HPV genotypes test was evaluated by multivariate logistic regression and area under the receiver operator characteristic curve (AUC)

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