TY  - JOUR
AU  - Lu, Zhen
AU  - Dong, Binhua
AU  - Cai, Hongning
AU  - Tian, Tian
AU  - Wang, Junfeng
AU  - Fu, Leiwen
AU  - Wang, Bingyi
AU  - Zhang, Weijie
AU  - Lin, Shaomei
AU  - Tuo, Xunyuan
AU  - Wang, Juntao
AU  - Yang, Tianjie
AU  - Huang, Xinxin
AU  - Zheng, Zheng
AU  - Xue, Huifeng
AU  - Xu, Shuxia
AU  - Liu, Siyang
AU  - Sun, Pengming
AU  - Zou, Huachun
PY  - 2025
DA  - 2025/3/19
TI  - Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study
JO  - JMIR Public Health Surveill
SP  - e67840
VL  - 11
KW  - cervical cancer
KW  - human papillomavirus
KW  - screening
KW  - machine learning
KW  - cervical tumor
KW  - cancer
KW  - carcinoma
KW  - tumor
KW  - malignant
KW  - ML
KW  - phenomapping strategy
KW  - logistic regression
KW  - regression
KW  - population-based
KW  - validation study
KW  - cancer prevention
KW  - validity
KW  - usability
KW  - algorithm
KW  - surveillance
KW  - electronic health record
KW  - EHR
AB  - Background: Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. Objective: This study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. Methods: We explored the data-driven CCP subgroups by applying unsupervised machine learning to a deeply phenotyped, population-based discovery cohort. We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. Results: This study included 551,934 women (median age, 49 years) in the discovery cohort and 47,130 women (median age, 37 years) in the external cohort. Phenotyping identified 5 CCP subgroups, with CCP4 showing the highest carcinoma prevalence. CCP2–4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03‐2.12], CCP3: 3.88 [3.78‐3.97], and CCP4: 4.47 [4.33‐4.63]) and CIN3+ (CCP2: 2.10 [2.05‐2.14], CCP3: 3.92 [3.82‐4.02], and CCP4: 4.45 [4.31‐4.61]) compared to CCP1 (P<.001), consistent with the direction of results observed in the external cohort. The proposed triple strategy was validated as clinically relevant, prioritizing high-risk subgroups (CCP3-4) for colposcopies and scaling human papillomavirus screening for CCP1-2. Conclusions: This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy. 
SN  - 2369-2960
UR  - https://publichealth.jmir.org/2025/1/e67840
UR  - https://doi.org/10.2196/67840
DO  - 10.2196/67840
ID  - info:doi/10.2196/67840
ER  -