Biomedical Research

Introduction: HPV testing is more sensitive than cytology for cervical cancer screening but leads to over referral to colposcopy. This study was designed to evaluate the correlation of development of high-grade cervical intraepithelial neoplasia and cancer with HPV infection tested by real-time fluorescent polymerase chain reaction and conventional hc2 respectively and to find a cut-off value of HPV tests to increase the specificity of HPV testing and facilitate the triage before colposcopy.

Material and Methods: Retrospective analysis the clinical data of 21,550 outpatients receiving cervical cancer screening, real-time PCR and hc2 testing were randomly chosen to test samples. Colposcopy with biopsies was performed to high-risk patients. Histopathology was taken as the diagnostic gold standard. Univariate ANOVA and multivariate binary logistic regression were adopted for data analysis.

Results: The sensitivity and positive predictive value was 98.9% and 40.4% of hc2, 93.8% and 42.6% of RT-PCR respectively. Spearman rank correlation coefficient of viral load and lesions is 0.246, P=0.000. Comparing with HPV negative cases, the risk of having HSIL and cervical cancer for 16/18 genotypes positive cases was 11.7 fold-higher (OR=11.71, 95% CI: 3.75-36.61). The risk for other 11 genotypes positive cases was 3.1 fold-higher (OR=3.10, 95% CI: 0.97-9.85). The risk for high-level of viral load group (1 × 10⁵~10⁷) was significantly higher than the negative group (OR=1.98, 95% CI: 1.43-2.74).

Conclusion: Real-time fluorescent polymerase chain reaction HPV detection method has better specificity and positive predictive value than conventional hc2. There is no optimal cut-off value of HPV viral load by hc2 to predict the development of cervical lesions. Patients with positive HPV 16/18 and high-level viral load by RT-PCR test have more risk to develop high-grade cervical lesions and carcinoma. Screening specificity is increased when taking these two cut-offs as the criteria to triage before colposcopy.

Author(s): Xinxin Du, Shufang Jiang, Aijun Liu, Yurong Fu, Yun Zhang, Yuanguang Meng
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