[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117243-en":3,"doc-seo-117243-105":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},117243,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Predicting the Recurrence of Ovarian Cancer Based on Machine Learning","Recurrence is the dominant cause of poor prognosis in ovarian cancer, yet robust prognostic biomarkers remain limited. This study builds a machine learning prediction model using multiple biomarkers and clinical parameters to estimate ovarian cancer recurrence risk. Data from 277 patients were split into training and testing cohorts, with prediction features derived from 47 clinical variables. Among six supervised clustering algorithms, XGBoost achieved the highest accuracy (0.95). Key biomarkers included neoadjuvant chemotherapy, MONO%, HCT, PAB, AST, and CA125, supporting more precise recurrence assessment and aiding personalized decision-making.","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  \nPredicting the Recurrence of Ovarian Cancer Based on Machine Learning  \nLining Zhou 1 , *, Hong Hong 2 , *, Fuying Chu 1 , Xiang Chen 1 , Chenlu Wang 1  \n1Department of Clinical Laboratory, The Second Affiliated Hospital of Nantong University and Nantong City No. 1 People’s Hospital, Nantong,  \nPeople’s Republic of China; 2Department of Clinical Laboratory, Nantong Traditional Chinese Medicine Hospital, Nantong, People’s Republic of China *These authors contributed equally to this work  \nCorrespondence: Chenlu Wang; Xiang Chen, [Email wangchenlu0101@163.com](Email wangchenlu0101@163.com); [ntchenx0520@163.com](ntchenx0520@163.com)  \n\n| Background: Recurrence is the main factor for poor prognosis in ovarian cancer, but few prognostic biomarkers were reported. In this study, we used machine learning methods based on multiple biomarkers to develop a specific prediction model for the recurrence of ovarian cancer.\u003Cbr>Methods: A total of 277 ovarian cancer patients were enrolled in this study and randomly classified into training and testing cohorts. The prediction information was obtained through 47 clinical parameters using six supervised clustering machine learning algorithms, including K-Nearest Neighbor (K-NN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost) .\u003Cbr>Results: In predicting the recurrence of ovarian cancer, machine learning algorithm was superior to conventional logistic regression analysis. In this study, XGBoost showed the best performance in predicting the recurrence of ovarian cancer, with an accuracy of 0.95. In addition, neoadjuvant chemotherapy, Monocyte ratio (MONO%), Hematocrit (HCT), Prealbumin (PAB), Aspartate aminotransferase (AST), and carbohydrate antigen 125 (CA125) are the most important biomarkers to predict the recurrence of ovarian cancer. Conclusion: The machine learning techniques can achieve a more accurate assessment of the recurrence of ovarian cancer, which can help clinicians make decisions, and develop personalized treatment strategies.\u003Cbr>Keywords: ovarian cancer, recurrence, machine learning, biomarkers, predictive modeling |\n| --- |\n| Background\u003Cbr>Ovarian cancer is one of the vital causes of gynecological cancer deaths, 1,2 and its lethality mainly comes from its high risk of recurrence.3 The recurrence rate of patients in the first three years is approximately 70% .4 Therefore, identifying the recurrence of ovarian cancer patients is important as it can guide personalized treatment and surveillance plans, such as the selection of chemotherapy.5 The carbohydrate antigen 125 (CA125) is routinely used to detect the progression of ovarian cancer,6 however, monitoring the increase in CA125 concentration alone for recurrence treatment cannot improve patient survival.7 With the emergence of different biomarkers, the rising number of studies on CA125 combined with different biomarkers seems to be promising. However, due to the lack of sensitivity or specificity, none of the biomarkers\u003Cbr>is used clinically to detect ovarian cancer progression, including carcinoembryonic antigen (CEA), carbohydrate antigen 19–9 (CA19-9), and HE4 are effective.8 Therefore, it is urgent to adopt new methods to predict the recurrence of ovarian cancer.\u003Cbr>In order to improve the effective treatment of ovarian cancer, it is significant to identify factors which can accurately define patient characteristics before initial intervention. In addition, developing methods for predicting treatment outcomes and prognosis is an important measure in the field of personalized medicine.9–11 Several studies have shown that the combination of biomarker and multiple clinical factors can accurately predict the prognosis.12, 1","cbCaidZ1KGkFBunA","https://ap.wps.com/l/cbCaidZ1KGkFBunA","pdf",5714501,1,13,"English","en",105,"# Background\n## Recurrence burden and biomarker limitations\n# Methods\n## Patient cohort and feature extraction\n# Results\n## Model comparison and key biomarkers\n# Conclusion\n## Clinical value of ML-based prediction","[{\"question\":\"Why is predicting ovarian cancer recurrence important?\",\"answer\":\"Recurrence is the main factor driving poor prognosis, with about 70% of patients recurring within the first three years. Early identification supports personalized treatment and surveillance decisions.\"},{\"question\":\"How was the machine learning prediction model constructed?\",\"answer\":\"The study enrolled 277 ovarian cancer patients and randomly assigned them to training and testing cohorts. Predictions used 47 clinical parameters and six supervised clustering machine learning algorithms, including K-NN, DT, RF, AdaBoost, GBM, and XGBoost.\"},{\"question\":\"Which algorithm performed best and which biomarkers were most important?\",\"answer\":\"XGBoost showed the best performance, reaching an accuracy of 0.95. Important biomarkers included neoadjuvant chemotherapy, MONO%, HCT, PAB, AST, and CA125.\"}]","Predicting the Recurrence of Ovarian Cancer Based on Machine Learning | PDF",1785674638,33,{"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},"predicting-the-recurrence-of-ovarian-cancer-based-on-machine-learning","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-recurrence-of-ovarian-cancer-based-on-machine-learning/117243/",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},"Why is predicting ovarian cancer recurrence important?","Question",{"text":75,"@type":76},"Recurrence is the main factor driving poor prognosis, with about 70% of patients recurring within the first three years. Early identification supports personalized treatment and surveillance decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning prediction model constructed?",{"text":80,"@type":76},"The study enrolled 277 ovarian cancer patients and randomly assigned them to training and testing cohorts. Predictions used 47 clinical parameters and six supervised clustering machine learning algorithms, including K-NN, DT, RF, AdaBoost, GBM, and XGBoost.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performed best and which biomarkers were most important?",{"text":84,"@type":76},"XGBoost showed the best performance, reaching an accuracy of 0.95. Important biomarkers included neoadjuvant chemotherapy, MONO%, HCT, PAB, AST, and CA125.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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"]