{"title":"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。","authors":"Maodan Huang, Xiaohong Chen, Xin Lin, Yuxiang Yang, Lu Liu, Youzhong Zhang, Ronglong Wang, Wei Chen","doi":"10.2147/RMHP.S536347","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"18 ","pages":"2921-2934"},"PeriodicalIF":2.0000,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423439/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prediction Models of Microinvasive Cervical Cancer in High-Grade Squamous Intraepithelial Lesion Treatment by Loop Electrosurgical Excision Procedure.\",\"authors\":\"Maodan Huang, Xiaohong Chen, Xin Lin, Yuxiang Yang, Lu Liu, Youzhong Zhang, Ronglong Wang, Wei Chen\",\"doi\":\"10.2147/RMHP.S536347\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":56009,\"journal\":{\"name\":\"Risk Management and Healthcare Policy\",\"volume\":\"18 \",\"pages\":\"2921-2934\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423439/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Management and Healthcare Policy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/RMHP.S536347\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Management and Healthcare Policy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/RMHP.S536347","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
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.
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.