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Background <p>High-grade cervical intraepithelial neoplasia (CIN2/3) is a critical precursor to cervical cancer, yet current screening methods (e.g., HPV testing, colposcopy) face challenges in accessibility and invasiveness, especially in resource-limited settings. We aimed to develop a non-invasive, machine learning (ML)-based model using routine blood biomarkers. This model is intended to assess the risk of high-grade CIN and potentially serve as a triage tool before colposcopy.</p> Methods <p>Data were collected from two groups: 128 high-grade CIN (CIN2/3) and 120 low-grade CIN (CIN1) patients. A total of 29 clinical characteristics and blood test measurements were considered for use in model development. Four feature selection algorithms (F-test, LASSO regression, decision tree, and random forest) were used to identify key predictors, and 11 machine learning algorithms were employed for model training. The dataset was split into training (70%) and testing (30%) cohorts. Model performance was evaluated using learning curves, receiver operating characteristic curves (ROC), area under the curve (AUC), Brier score, calibration curves, Precision-Recall (PR) curves, and Decision Curve Analysis (DCA). A web-based calculator was developed for clinical deployment. We assessed feature importance using the SHapley Additive exPlanation (SHAP) approach.</p> Results <p>Key features selected for the model included creatinine (CREA), red blood cell count (RBC), neutrophil ratio (NEU%), direct bilirubin (DBIL), and monocyte count (MON). The Support Vector Machine (SVM) algorithm achieved the best predictive performance, with an AUC of 0.75 (95% CI: 0.69–0.80) and a Brier score of 0.21 (95% CI: 0.17–0.28). By employing the SHAP method, we identified the variables that contributed to the model. The web tool (<a href="https://dvhl6xsf29zmdewixjx7kz.streamlit.app">https://dvhl6xsf29zmdewixjx7kz.streamlit.app</a>) provides real-time risk stratification.</p> Conclusions <p>The model demonstrated strong performance across various validation metrics, with the SVM algorithm achieving an AUC of 0.75, indicating potential clinical utility. We also developed a web-based calculator to estimate high-grade CIN.</p>

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Machine learning in early screening for high-grade cervical intraepithelial neoplasia using blood testing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • Congbo Yue,
  • Shichao Liu,
  • Wenhua Wang,
  • Yu Zhao,
  • Xiaofeng Zhang,
  • Guanghui Zhao

摘要

Background

High-grade cervical intraepithelial neoplasia (CIN2/3) is a critical precursor to cervical cancer, yet current screening methods (e.g., HPV testing, colposcopy) face challenges in accessibility and invasiveness, especially in resource-limited settings. We aimed to develop a non-invasive, machine learning (ML)-based model using routine blood biomarkers. This model is intended to assess the risk of high-grade CIN and potentially serve as a triage tool before colposcopy.

Methods

Data were collected from two groups: 128 high-grade CIN (CIN2/3) and 120 low-grade CIN (CIN1) patients. A total of 29 clinical characteristics and blood test measurements were considered for use in model development. Four feature selection algorithms (F-test, LASSO regression, decision tree, and random forest) were used to identify key predictors, and 11 machine learning algorithms were employed for model training. The dataset was split into training (70%) and testing (30%) cohorts. Model performance was evaluated using learning curves, receiver operating characteristic curves (ROC), area under the curve (AUC), Brier score, calibration curves, Precision-Recall (PR) curves, and Decision Curve Analysis (DCA). A web-based calculator was developed for clinical deployment. We assessed feature importance using the SHapley Additive exPlanation (SHAP) approach.

Results

Key features selected for the model included creatinine (CREA), red blood cell count (RBC), neutrophil ratio (NEU%), direct bilirubin (DBIL), and monocyte count (MON). The Support Vector Machine (SVM) algorithm achieved the best predictive performance, with an AUC of 0.75 (95% CI: 0.69–0.80) and a Brier score of 0.21 (95% CI: 0.17–0.28). By employing the SHAP method, we identified the variables that contributed to the model. The web tool (https://dvhl6xsf29zmdewixjx7kz.streamlit.app) provides real-time risk stratification.

Conclusions

The model demonstrated strong performance across various validation metrics, with the SVM algorithm achieving an AUC of 0.75, indicating potential clinical utility. We also developed a web-based calculator to estimate high-grade CIN.