微创宫颈癌高级别鳞状上皮内病变环形电切治疗的预测模型。

IF 2 4区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Risk Management and Healthcare Policy Pub Date : 2025-09-06 eCollection Date: 2025-01-01 DOI:10.2147/RMHP.S536347
Maodan Huang, Xiaohong Chen, Xin Lin, Yuxiang Yang, Lu Liu, Youzhong Zhang, Ronglong Wang, Wei Chen
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引用次数: 0

摘要

目的:在行环电切术(LEEP)的高级别鳞状上皮内病变(HSIL)患者中实施全面的微创宫颈癌(MIC)风险评估对优化治疗策略和改善患者预后至关重要。方法:2017年3月至2024年1月,回顾性纳入两家医院3066例符合条件的HSIL患者,分为1个培训队列(n = 2084)、1个内部验证队列(579)和1个外部测试队列(n = 403)。采用四种特征选择方法(随机森林、Lasso回归、Boruta算法和极端梯度增强)从训练队列中识别关键预测因素。然后,开发了四种机器学习模型,并使用综合指标进行了评估。通过可解释技术将最佳模型可视化,并将其作为基于网络的临床决策支持系统,用于现实世界的实施。结果:确定了6个临床预测变量,包括手术切缘、宫颈刮除(ECC)、TCT状态、HPV状态、转化区(TZ)类型和年龄。最优模型显示出良好的预测性能,在内部验证队列中,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,AUC为0.802 (95% CI: 0.730-0.874)。结论:基于机器学习的模型可以准确评估LEEP治疗HSIL期间MIC的风险,可能有助于临床实践中选择合适的治疗和监测策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction Models of Microinvasive Cervical Cancer in High-Grade Squamous Intraepithelial Lesion Treatment by Loop Electrosurgical Excision Procedure.

Prediction Models of Microinvasive Cervical Cancer in High-Grade Squamous Intraepithelial Lesion Treatment by Loop Electrosurgical Excision Procedure.

Prediction Models of Microinvasive Cervical Cancer in High-Grade Squamous Intraepithelial Lesion Treatment by Loop Electrosurgical Excision Procedure.

Prediction Models of Microinvasive Cervical Cancer in High-Grade Squamous Intraepithelial Lesion Treatment by Loop Electrosurgical Excision Procedure.

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

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来源期刊
Risk Management and Healthcare Policy
Risk Management and Healthcare Policy Medicine-Public Health, Environmental and Occupational Health
CiteScore
6.20
自引率
2.90%
发文量
242
审稿时长
16 weeks
期刊介绍: Risk Management and Healthcare Policy is an international, peer-reviewed, open access journal focusing on all aspects of public health, policy and preventative measures to promote good health and improve morbidity and mortality in the population. Specific topics covered in the journal include: Public and community health Policy and law Preventative and predictive healthcare Risk and hazard management Epidemiology, detection and screening Lifestyle and diet modification Vaccination and disease transmission/modification programs Health and safety and occupational health Healthcare services provision Health literacy and education Advertising and promotion of health issues Health economic evaluations and resource management Risk Management and Healthcare Policy focuses on human interventional and observational research. The journal welcomes submitted papers covering original research, clinical and epidemiological studies, reviews and evaluations, guidelines, expert opinion and commentary, and extended reports. Case reports will only be considered if they make a valuable and original contribution to the literature. The journal does not accept study protocols, animal-based or cell line-based studies.
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