Cancer Res Treat.  2025 Apr;57(2):547-557. 10.4143/crt.2024.465.

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

• Song H
• Lee HY
• Oh SA
• Seong J
• Hur SY
• Choi YJ

• KMID: 2566871
• DOI: http://doi.org/10.4143/crt.2024.465

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) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates.
Results
Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination.
Conclusion
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.

Keyword

Human papillomavirus; Risk stratification; Atypical squamous cells of the cervix; Squamous intraepithelial lesions

Figure

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  Fig. 1. Study design. ASCUS, atypical squamous cells of undetermined significance; CIN, cervical intraepithelial neoplasm; HPV, human papillomavirus; LSIL, low-grade squamous intraepithelial lesion.

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  Fig. 2. Definition of the types of human papillomavirus (HPV) infection. Modified from Park et al. J Gynecol Oncol. 2019;30:e50, with permission of Korean Society of Gynecologic Oncology [8].

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  Fig. 3. Human papillomavirus subgroups as genotypes. Hr, high-risk; Lr, low-risk.

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  Fig. 4. Multivariable Cox analysis by human papillomavirus (HPV) infection type (hazard ratios and 95% confidence intervals were adjusted by diagnostic age, body mass index (BMI), multiple HPV infection, and sexually transmitted disease infection history. (A) HPV-persistent infection. (B) HPV–non-persistent infection. Hr1, HPV 16, 18, 31, 33, 45, 52, 58; Hr2, HPV 35, 39, 51, 56, 59, 66, 68; Lr, low risk HPV.

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  Fig. 5. Analysis of gradient boosting model. (A) Importance of prediction for progression. (B) Average increase in the area under the curve per time interval. HPV, human papillomavirus.

Reference

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