Cumulative risk of cervical intraepithelial neoplasia for women with normal cytology but positive for human papillomavirus: Systematic review and meta-analysis.
Talía MalagónKarena D VoleskySheila BoutenClaudie LapriseEduardo L FrancoEduardo L FrancoPublished in: International journal of cancer (2020)
Most women positive for human papillomavirus (HPV) are cytology normal. The optimal screen-management of these women is unclear given their risk of developing precancer. We performed a systematic review and meta-analysis of progression rates to precancer and cancer for HPV-positive, cytology normal women. We searched MEDLINE, EMBASE and Scopus for prospective studies measuring the cumulative incidence of precancer and cervical cancer in HPV-positive, cytology/histology normal women. Record screening was performed independently by two reviewers. We modeled the cumulative incidence over time using a multilevel random-effects meta-regression model. We used the model to predict HPV type-specific risks of precancer and cancer over follow-up. Data from 162 unique records were used in our analysis. The average incidence rate of cervical intraepithelial neoplasia grade 3 or cancer (CIN3+) in high-risk HPV positive but cytology/histology normal women was 1.0 per 100 women-years (95% CI: 1.0-1.1). This corresponds to an average cumulative risk at 1, 3 and 5 years of 2.1% (95% prediction interval 0.0-9.5), 4.3% (95% prediction interval 0.0-11.5) and 6.4% (95% prediction interval 0.0-13.5). HPV type was a strong predictor of the risk of oncogenic progression. There was substantial heterogeneity in the background precancer risk across studies (P-value < .0001). Our HPV type-specific progression risk estimates can help inform risk-based cervical cancer screening guidelines for HPV-positive women. However, precancer and cervical cancer risks are highly variable and may not be generalizable between populations.
Keyphrases
- cervical cancer screening
- high grade
- polycystic ovary syndrome
- pregnancy outcomes
- papillary thyroid
- breast cancer risk
- fine needle aspiration
- squamous cell carcinoma
- transcription factor
- climate change
- machine learning
- ultrasound guided
- electronic health record
- adipose tissue
- deep learning
- clinical practice
- childhood cancer
- lymph node metastasis