CIN2 + detection in high-risk HPV patients with no or minor cervical cytologic abnormalities: a clinical approach validated by machine learning.

Purpose: To evaluate the feasibility and diagnostic value of the combination of colposcopy, cytology and hrHPV (high-risk human papilloma virus) PCR (polymerase chain reaction) testing in patients with no or minor cytologic abnormalities and HPV high risk infection and to find the best predictors for the presence of CIN2 + in this patient collective.

Methods: Three hundred and thirty-four hrHPV patients with normal cytology or minor cytologic abnormalities who had a colposcopic examination at the center of colposcopy at the university hospital Aachen in 2021 were enrolled in this retrospective cohort analysis. Multivariate logistic regression and a machine-learning technique (random forests, leave-one-out analysis) were used.

Results: The overall risk for CIN2 + in hrHPV-positive patients with normal cytology was 7.7% (N = 18) (5% for CIN3 +), 18% (N = 16) (10.1% for CIN3 +) in patients with PAP IIp (ASC-US) and 62.5% (N = 5) (25% for CIN3 +) in patients with PAP IIg (AGC). Variables that show a statistically significant influence for the CIN-status are 'major change' as the result of colposcopy, transformation zone type T1, PAP IIg upon referral (AGC) and hrHPV category 1a (HPV 16/18) detection. Using machine learning (random forests) techniques, the main influencing variables were confirmed. A monotonously decreasing risk for CIN2 + from hrHPV category 1a to 3 (in accordance to the IACR guidelines) was found.

Conclusion: In the collective of hrHPV patients with no or minor cytologic abnormalities, the result of colposcopy and HPV PCR status are key predictors for the detection of CIN2 + with a monotonously decreasing risk for CIN2 + from hrHPV category 1a to 3.