[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117231-en":3,"doc-seo-117231-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117231,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",7,"Healthcare","Machine Learning Prediction of Residual and Recurrent High-Grade CIN Post-LEEP - Predictive Modeling","Machine learning is used to address the need for personalized post-operative surveillance after loop electrosurgical excision procedure (LEEP) for high-grade cervical intraepithelial neoplasia (CIN). A retrospective cohort of 532 patients is analyzed to identify residual or recurrent high-grade CIN (CIN2 or worse) within five years. Multiple feature selection methods and eight ML algorithms are compared using AUROC, accuracy, sensitivity, specificity, PPV, NPV, calibration, and decision curve analysis. The final XGBoost model achieves the strongest discrimination and yields an online risk prediction tool.","Cancer Management and Research  \nCancer Management and Research downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \n Open Access Full Text Article ORIGINAL RESEARCH  \nMachine Learning Prediction of Residual and Recurrent High-Grade CIN Post-LEEP  \nFurui Zhai , Shanshan Mu, Yinghui Song, Min Zhang, Cui Zhang, Ze Lv Gynecological Clinic, Cangzhou Central Hospital, Cangzhou City, Hebei Province, People’s Republic of China  \nCorrespondence: Furui Zhai, Department of Gynecological Clinic, Cangzhou Central Hospital, 16 Xinhua West Road, Cangzhou City, Hebei Province, People’s Republic of China, Tel +86-0317-2075783, Email [zfr860708@126.com](zfr860708@126.com)  \n\n| Purpose: This study aims to develop a machine learning (ML) model to predict the risk of residual or recurrent high-grade cervical intraepithelial neoplasia (CIN) after loop electrosurgical excision procedure (LEEP), addressing a critical gap in personalized followup care.\u003Cbr>Methods: A retrospective analysis of 532 patients who underwent LEEP for high-grade CIN at Cangzhou Central Hospital (2016– 2020) was conducted. In the final analysis, 99 women (18.6%) were found to have residual or recurrent high-grade CIN (CIN2 or worse) within five years of follow-up. Four feature selection methods identified significant predictors of residual or recurrent CIN. Eight ML algorithms were evaluated using performance metrics such as AUROC, accuracy, sensitivity, specificity, PPV, NPV, F1 score, calibration curve, and decision curve analysis. Fivefold cross-validation optimized and validated the model, and SHAP analysis assessed feature importance.\u003Cbr>Results: The XGBoost algorithm demonstrated the highest predictive performance with the best AUROC. The optimized model included six key predictors: age, ThinPrep cytologic test (TCT) results, HPV classification, CIN severity, glandular involvement, and margin status. SHAP analysis identified CIN severity and margin status as the most influential predictors. An online prediction tool was developed for real-time risk assessment.\u003Cbr>Conclusion: This ML-based predictive model for post-LEEP high-grade CIN provides a significant advancement in gynecologic oncology, enhancing personalized patient care and facilitating early intervention and informed clinical decision-making.\u003Cbr>Keywords: cervical intraepithelial neoplasia, loop electrosurgical excision procedure, residual or recurrent, machine learning, predictive modeling |\n| --- |\n| Introduction\u003Cbr>Globally, cervical cancer ranks as the fourth most common and deadly cancer, with varying trends in incidence and mortality between developed and developing nations.1,2 While developed countries are experiencing a decrease in cases, there is a concerning rise in China, with an estimated 110,000 new cases and 60,000 deaths projected by 2020.3 This disparity underscores the critical need for structured screening programs to detect and intervene early in precancerous lesions, particularly cervical intraepithelial neoplasia (CIN2+), which is essential for preventing cervical cancer.\u003Cbr>Cervical intraepithelial neoplasia (CIN), a significant precursor to invasive cervical cancer (ICC), has a significant impact on the gynecological health of women, especially those of reproductive age.4,5 Left untreated, high-grade CIN can progress to ICC, highlighting the challenge of predicting lesion outcomes and emphasizing the need for effective surgical interventions like loop electrosurgical excision procedure (LEEP) and cold knife conization (CKC) .6 While these treatments are generally successful, a small percentage of cases (5% to 25%) may still be at risk of developing residual or recurrent high-grade lesions, increasing the likelihood of progression to ICC.7\u003Cbr>Effective post-operative management is crucial for preventing residual or recurrent lesions after surgical interventions for high-grade CIN. Persistent high-risk human papillomavirus (HR-HPV) infection is","cbCaikiPykeY1qjy","https://ap.wps.com/l/cbCaikiPykeY1qjy","pdf",6174880,1,13,"English","en",105,"# Introduction\n# Methods\n## Feature selection and predictors\n## Machine learning models and validation\n# Results\n## Model performance\n## Key predictors and interpretability\n## Online prediction tool\n# Conclusion","[{\"question\":\"What clinical problem does the study target after LEEP?\",\"answer\":\"The study targets predicting the risk of residual or recurrent high-grade CIN after LEEP, enabling more personalized follow-up care.\"},{\"question\":\"How was the prediction model developed and validated?\",\"answer\":\"It was built from 532 retrospective patients and evaluated across eight ML algorithms using metrics such as AUROC and calibration, with fivefold cross-validation for optimization and validation.\"},{\"question\":\"Which predictors were most influential in the best-performing model?\",\"answer\":\"SHAP analysis indicated that CIN severity and margin status were the most influential predictors, and the final optimized model included six key variables including age, TCT results, HPV classification, glandular involvement, and margin status.\"}]","Machine Learning Prediction of Residual and Recurrent High-Grade CIN Post-LEEP - Predictive Modeling | PDF",1785674570,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-prediction-of-residual-and-recurrent-high-grade-cin-post-leep-predictive-modeling","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-prediction-of-residual-and-recurrent-high-grade-cin-post-leep-predictive-modeling/117231/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical problem does the study target after LEEP?","Question",{"text":75,"@type":76},"The study targets predicting the risk of residual or recurrent high-grade CIN after LEEP, enabling more personalized follow-up care.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the prediction model developed and validated?",{"text":80,"@type":76},"It was built from 532 retrospective patients and evaluated across eight ML algorithms using metrics such as AUROC and calibration, with fivefold cross-validation for optimization and validation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which predictors were most influential in the best-performing model?",{"text":84,"@type":76},"SHAP analysis indicated that CIN severity and margin status were the most influential predictors, and the final optimized model included six key variables including age, TCT results, HPV classification, glandular involvement, and margin status.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]