[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128158-en":3,"doc-seo-128158-105":31,"detail-sidebar-cat-0-en-105":96},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128158,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",7,"Healthcare","Who benefits from adjuvant chemotherapy? - Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative-intent resection using machine learning algorithms","Objective: enhance identification of early recurrence in intrahepatic cholangiocarcinoma (ICC) patients after curative-intent resection and determine which patients could benefit from adjuvant chemotherapy (ACT). Methods: 254 ICC patients were analyzed to identify early-recurrence predictors using logistic regression and feature-importance assessment, then building machine learning models from the top five predictors. Results: early recurrence independently predicted overall survival; LightGBM performed best and suggested ACT improved median OS and RFS for patients predicted to experience early recurrence. Conclusion: the models can stratify patients for ACT.","TYPE Original Research PUBLISHED 06 June 2025  \nDOI 10.3389/fonc.2025.1594200  \nOPEN ACCESS  \nEDITED BY  \nZhaohui Tang,  \nShanghai Jiao Tong University, China  \nREVIEWED BY  \nPaul David,  \nUniversity Hospital Erlangen, Germany Qiang Yan,  \nHuzhou Central Hospital, China Jiangtao Li,  \nZhejiang University, China  \n*CORRESPONDENCE  \nZhimin Geng  \n [gengzhimin@mail.xjtu.edu.cn](gengzhimin@mail.xjtu.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 15 March 2025  \nACCEPTED 22 May 2025  \nPUBLISHED 06 June 2025  \nCITATION  \nLi Q, Liu H, Ma Y, Tang Z, Chen C, Zhang D and Geng Z (2025) Who benefits from adjuvant chemotherapy? Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curativeintent resection using machine learning algorithms.  \nFront. Oncol. 15:1594200 .  \ndoi: 10.3389/fonc.2025.1594200  \nCOPYRIGHT  \n© 2025 Li, Liu, Ma, Tang, Chen, Zhang and Geng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nWho benefits from adjuvant chemotherapy? Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative-intent resection using machine learning algorithms  \nQi Li †, Hengchao Liu †, Yubo Ma, Zhenqi Tang, Chen Chen, Dong Zhang and Zhimin Geng*  \nDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China  \nObjective: It is vital to enhance the identification of early recurrence in intrahepatic cholangiocarcinoma (ICC) patients after curative-intent resection and to determine which patients could benefit from adjuvant chemotherapy (ACT) . This study aimed to evaluate the effectiveness of machine learning algorithms in detecting early recurrence in ICC patients and select those who would benefit from ACT to improve prognosis.  \nMethods: The study analyzed 254 intrahepatic cholangiocarcinoma (ICC) patients who underwent curative-intent resection to identify early recurrence predictors. Through logistic regression and feature importance analysis, we determined key risk factors and subsequently developed machine learning models utilizing the top five predictors for early recurrence prediction. The predictive performance was validated across area under the ROC curve (AUC) .  \nResults: Early recurrence was an independent prognostic risk factor for overall survival (OS) in ICC patients after curative resection (P\u003C0 . 001) . The feature importance ranking based on machine learning algorithms showed that AJCC 8th edition N stage, number of tumors, T stage, perineural invasion, and CA125 asthe top five variables associated with early recurrence, which was consistent with the independent risk factors of multivariate logistic regression model. Using the aforementioned five variables, we developed four machine learning prediction models, including logistic regression, support vector machine, LightGBM, and random forest. In the training set, the AUC values were 0 . 849, 0 . 860, 0 . 852, and 0. 850, respectively. In the testing set, the AUC values were 0 . 804, 0 . 807, 0 . 841, and 0 . 835, respectively. Among the various prediction models, LightGBM demonstrated superior performance compared to other models in the testing set, exhibiting higher sensitivity, specificity, and accuracy. The effectiveness of ACT on prognosis for different recurrence times, as predicted by the LightGBM model, indicated that ACT could significantly prolong median OS and RFS for ICC patients predicted to experience early recurrence in both the training and testing  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nsets (P\u003C0 . 05) . ","cbCaihC6DVW1skqh","https://ap.wps.com/l/cbCaihC6DVW1skqh","pdf",2092883,2,1,12,"English","en",105,"# Objective\n# Methods\n## Predictors and model development\n# Results\n## Prognostic impact of early recurrence\n## Performance of machine learning models\n## ACT benefit by recurrence timing\n# Conclusion","[{\"question\":\"What was the main objective of this study in ICC patients?\",\"answer\":\"To identify early recurrence in intrahepatic cholangiocarcinoma after curative-intent resection and determine which patients would benefit from adjuvant chemotherapy to improve prognosis.\"},{\"question\":\"How were early recurrence predictors selected?\",\"answer\":\"The study used logistic regression and feature-importance analysis on 254 patients, then selected the top five predictors for early recurrence modeling.\"},{\"question\":\"Which machine learning model performed best for early recurrence prediction?\",\"answer\":\"LightGBM showed superior performance in the testing set, with higher sensitivity, specificity, and accuracy than the other compared models.\"},{\"question\":\"Does adjuvant chemotherapy help all ICC patients after curative resection?\",\"answer\":\"According to LightGBM predictions, ACT significantly prolonged median overall survival and recurrence-free survival for patients predicted to experience early recurrence, while it did not improve outcomes for patients predicted to have late recurrence.\"}]","Who benefits from adjuvant chemotherapy? - Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative-intent resection using machine learning algorithms | PDF",1785945188,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":91,"head_meta":93,"extra_data":95,"updated_unix":29},"who-benefits-from-adjuvant-chemotherapy-identification-of-early-recurrence-in-intrahepatic-cholangiocarcinoma-patients-after-curative-intent-resection-using-machine-learning-algorithms","",{"@graph":37,"@context":90},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/who-benefits-from-adjuvant-chemotherapy-identification-of-early-recurrence-in-intrahepatic-cholangiocarcinoma-patients-after-curative-intent-resection-using-machine-learning-algorithms/128158/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"What was the main objective of this study in ICC patients?","Question",{"text":76,"@type":77},"To identify early recurrence in intrahepatic cholangiocarcinoma after curative-intent resection and determine which patients would benefit from adjuvant chemotherapy to improve prognosis.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were early recurrence predictors selected?",{"text":81,"@type":77},"The study used logistic regression and feature-importance analysis on 254 patients, then selected the top five predictors for early recurrence modeling.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performed best for early recurrence prediction?",{"text":85,"@type":77},"LightGBM showed superior performance in the testing set, with higher sensitivity, specificity, and accuracy than the other compared models.",{"name":87,"@type":74,"acceptedAnswer":88},"Does adjuvant chemotherapy help all ICC patients after curative resection?",{"text":89,"@type":77},"According to LightGBM predictions, ACT significantly prolonged median overall survival and recurrence-free survival for patients predicted to experience early recurrence, while it did not improve outcomes for patients predicted to have late recurrence.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,123,127,132,135,139],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},40,"healthcare",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":30,"slug":126},8,"Research & Report","research-report",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":47,"category_name":141,"show_sort_weight":111,"slug":142},19,"General","general"]