[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126000-en":3,"doc-seo-126000-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126000,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b inpatients with advanced melanoma - original research","Interferon-alpha1b is explored as an adjuvant therapy for advanced melanoma, motivating development of predictive tools for postoperative benefit. Five machine learning models are trained and compared using retrospective data from 113 AJCC stage III–IV patients treated with IFN-a1b. Recurrence-free survival and overall survival are quantified with Kaplan–Meier methods, while model accuracy is assessed via C-index, time-dependent ROC curves, and decision curve analysis. The decision tree model achieves the best discrimination, and serum albumin emerges as a key prognostic predictor.","TYPE Original Research PUBLISHED 12 November 2024 DOI 10.3389/fimmu.2024.1495329  \nOPEN ACCESS  \nEDITED BY  \nJiajie Diao,  \nUniversity of Cincinnati, United States  \nREVIEWED BY  \nJianpeng Sheng,  \nNanyang Technological University, Singapore Yu Xu,  \nFudan University, China Liyun Dong,  \nHuazhong University of Science and Technology, China  \n*CORRESPONDENCE  \nYu Liu  \n [303480157@qq.com](303480157@qq.com)  \n†These authors have contributed equally to this work  \nRECEIVED 12 September 2024  \nACCEPTED 22 October 2024  \nPUBLISHED 12 November 2024  \nCITATION  \nJiang L, Su K, Wang J, Lin Y, Zhao X, Zhang Hand Liu Y (2024) The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b inpatients with advanced melanoma.  \nFront. Immunol. 15:1495329 .  \ndoi: 10.3389/fimmu.2024.1495329  \nCOPYRIGHT  \n© 2024 Jiang, Su, Wang, Lin, Zhao, Zhang and Liu. 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.  \nThe established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b in patients with advanced melanoma  \nLinhan Jiang 1†, Ke Su 2,3†, Jing Wang 1†, Yitong Lin 1, Xianya Zhao 1, Hengxiang Zhang 1 and Yu Liu 1*  \n1 Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi ’an, Shaanxi, China, 2 Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 3 Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China  \nBackground: Interferon-alpha1b (IFN-a1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five machine learning models to evaluate the efficacy of postoperative IFN-a1b inpatients with advanced melanoma.  \nMethods: We retrospectively analyzed 113 patients with the American Joint Committee on Cancer (AJCC) stage III-IV melanoma who received postoperative IFN-a1b therapy between July 2009 and February 2024 . Recurrence-free survival (RFS) and overall survival (OS) were assessed using Kaplan-Meier analysis. Five machine learning models (Decision Tree, Cox Proportional Hazards, Random Forest, Support Vector Machine, and LASSO regression) were developed and compared for their capacity to predict the outcomes of patients. Model performance was evaluated using concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis.  \nResults: The 1-year, 2-year, and 3-year RFS rates were 71 . 10%, 43 . 10%, and 31. 10%, respectively. For OS, the 1-year, 2-year, and 3-year OS rates were 99. 10%, 82 . 30%, and 75 . 00%, respectively. The Decision Tree (DT) model demonstrated superior predictive performance with the highest C-index of 0.792. Time-dependent ROC analysis for predicting 1-, 2-, and 3-year RFS based on the DT model is 0 .77, 0 .79 and 0 .76, respectively. Serum albumin emerged as the important predictor of RFS.  \nConclusions: Our study demonstrates the considerable efficacy DT model for predicting the efficacy of adjuvant IFN-a1b in patients with advanced melanoma. Serum albumin was identified as a key predictive factor of the treatment efficacy.  \nKEYWORDS  \nimmunotherapy, machine learning, melanoma, interferon-alpha, adjuvant therapy, prognostic factors  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nThe global incidence of melanoma has been increasing gradually, presenting a considerable public health challenge (1, 2) . The ","cbCainmcBa3bCXka","https://ap.wps.com/l/cbCainmcBa3bCXka","pdf",1329505,5,1,9,"English","en",105,"# Background\n## Methods\n## Results\n## Conclusions\n# Introduction\n## Global burden and clinical challenge\n## Adjuvant therapy rationale","[{\"question\":\"What is the study’s goal regarding interferon-alpha1b in advanced melanoma?\",\"answer\":\"To develop five machine learning models that evaluate the efficacy of postoperative interferon-alpha1b in patients with advanced melanoma.\"},{\"question\":\"How was the model performance evaluated in the study?\",\"answer\":\"Model performance used concordance index (C-index), time-dependent ROC curves for 1-, 2-, and 3-year outcomes, and decision curve analysis.\"},{\"question\":\"Which model showed the best predictive performance and what key predictor was identified?\",\"answer\":\"The Decision Tree model achieved the highest C-index (0.792). Serum albumin was identified as an important predictor of recurrence-free survival.\"}]","The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b inpatients with advanced melanoma - original research | PDF",1785902481,23,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"the-established-of-a-machine-learning-model-for-predicting-the-efficacy-of-adjuvant-interferon-alpha1b-inpatients-with-advanced-melanoma-original-research","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/the-established-of-a-machine-learning-model-for-predicting-the-efficacy-of-adjuvant-interferon-alpha1b-inpatients-with-advanced-melanoma-original-research/126000/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the study’s goal regarding interferon-alpha1b in advanced melanoma?","Question",{"text":77,"@type":78},"To develop five machine learning models that evaluate the efficacy of postoperative interferon-alpha1b in patients with advanced melanoma.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How was the model performance evaluated in the study?",{"text":82,"@type":78},"Model performance used concordance index (C-index), time-dependent ROC curves for 1-, 2-, and 3-year outcomes, and decision curve analysis.",{"name":84,"@type":75,"acceptedAnswer":85},"Which model showed the best predictive performance and what key predictor was identified?",{"text":86,"@type":78},"The Decision Tree model achieved the highest C-index (0.792). 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