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
In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.
Authors
- Youn Jin Choi (ORCID: https://orcid.org/0000-0003-4699-2116)
- Jaehyun Seong (ORCID: https://orcid.org/0000-0003-2345-3937)
- Hong Yeon Lee (ORCID: https://orcid.org/0000-0003-0843-4826)
- Heekyoung Song (ORCID: https://orcid.org/0000-0002-9786-3528)
- Shin Ah Oh
- Soo Young Hur
Institutions
- Korea National Institute of Health (KR)
- The Catholic University of Korea Incheon St. Mary's Hospital (KR)
- The Catholic University of Korea Yeouido St. Mary's Hospital (KR)
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
- Columbia University (US)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Cancer Research and Treatment
- Published
- 2024-09-06
- DOI
- https://doi.org/10.4143/crt.2024.465
- Citations
- 8
- Primary Topic
- Cervical Cancer and HPV Research
- Type
- article
- Field-Weighted Citation Impact
- 3.09
Funders
- National Research Foundation
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea