Huang Maodan, Chen Xiaohong, Lin Xin, Yang Yuxiang, Liu Lu, Zhang Youzhong, Wang Ronglong, Chen Wei
Department of Obstetrics and Gynecology, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, 363000, People's Republic of China.
Department of Obstetrics and Gynecology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, People's Republic of China.
Risk Manag Healthc Policy. 2025 Sep 6;18:2921-2934. doi: 10.2147/RMHP.S536347. eCollection 2025.
The implementation of comprehensive microinvasive cervical cancer (MIC) risk assessment in high-grade squamous intraepithelial lesion (HSIL) patients undergoing loop electrosurgical excision procedure (LEEP) is critical to optimize treatment strategies and improve patient outcomes.
From March 2017 to January 2024, a total of 3066 eligible patients with HSIL were retrospectively enrolled from two hospitals and assigned into one training cohort (n = 2084), one internal validation cohort (579) and one external testing cohort (n = 403). Four feature selection methods (Random Forest, Lasso regression, Boruta algorithm, and Extreme Gradient Boosting) were employed to identify key predictive factors from the training cohort. Then, four machine learning models were developed and evaluated using comprehensive metrics. The optimal model was visualized through interpretable techniques and operationalized as a web-based clinical decision support system for real-world implementation.
Six clinical predictive variables were identified, including surgical margins, endocervical curettage (ECC), TCT status, HPV status, Transformation Zone (TZ) type and Age. The optimal model demonstrated good predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.822 (95% CI: 0.793-0.852) in the internal validation cohort and 0.802 (95% CI: 0.730-0.874) in the external validation cohort.
The machine learning-based model can accurately assess the risk of MIC during the treatment of HSIL with LEEP, potentially aiding in the selection of appropriate treatment and surveillance strategies in clinical practice.
对接受环形电切术（LEEP）的高级别鳞状上皮内病变（HSIL）患者实施全面的微侵袭性宫颈癌（MIC）风险评估，对于优化治疗策略和改善患者预后至关重要。
2017年3月至2024年1月，从两家医院回顾性纳入3066例符合条件的HSIL患者，分为一个训练队列（n = 2084）、一个内部验证队列（579例）和一个外部测试队列（n = 403）。采用四种特征选择方法（随机森林、套索回归、博鲁塔算法和极端梯度提升）从训练队列中识别关键预测因素。然后，开发并使用综合指标评估四种机器学习模型。通过可解释技术将最佳模型可视化，并作为基于网络的临床决策支持系统投入实际应用。
确定了六个临床预测变量，包括手术切缘、宫颈管刮术（ECC）、TCT状态、HPV状态、转化区（TZ）类型和年龄。最佳模型表现出良好的预测性能，在内部验证队列中的受试者工作特征曲线下面积（AUC）为0.822（95%CI：0.793 - 0.852），在外部验证队列中为0.802（95%CI：0.730 - 0.874）。
基于机器学习的模型能够在LEEP治疗HSIL期间准确评估MIC风险，可能有助于临床实践中选择合适的治疗和监测策略。
