Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study.

ABSTRACT:

Purpose

We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL).

Materials and methods

Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) an

SUBMITTER: Song H 

PROVIDER: S-EPMC12016842 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature