[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127486-en":3,"doc-seo-127486-105":31,"detail-sidebar-cat-0-en-105":92},{"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},127486,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Establishment of Prognostic Models of Adrenocortical Carcinoma - Using Machine Learning and Big Data","Adrenocortical carcinoma (ACC) is a rare malignant tumor with poor life expectancy, making early identification of high-risk patients crucial for more aggressive treatment planning. This research builds prognostic models for ACC using machine learning with large-scale data. Clinical records are retrieved from the SEER database, filtered by inclusion and exclusion criteria, and modeled via BP-ANN, random forest, support vector machine, and naive Bayes with 10-fold cross-validation to evaluate predictive efficiency via AUROC.","TYPE Original Research PUBLISHED 06 January 2023 DOI 10.3389/fsurg.2022.966307  \nEDITED BY  \nWeihong Jiang,  \nCentral South University, China  \nREVIEWED BY  \nMariarita Tarallo,  \nSapienza University of Rome, Italy Pietro Locantore,  \nCatholic University of the Sacred Heart, Italy  \n*CORRESPONDENCE  \nZhe Xu  \n[xzhe@mail.sysu.edu.cn](xzhe@mail.sysu.edu.cn)  \nSPECIALTY SECTION  \nThis article was submitted to Surgical Oncology, a section of the journal Frontiers in Surgery  \nRECEIVED 10 June 2022  \nACCEPTED 21 November 2022  \nPUBLISHED 06 January 2023  \nCITATION  \nTang J, Fang Y and Xu Z (2023) Establishment of prognostic models of adrenocortical carcinoma using machine learning and big data.  \nFront. Surg. 9:966307 .  \ndoi: 10.3389/fsurg.2022.966307  \nCOPYRIGHT  \n© 2023 Tang, Fang and Xu. This is an openaccess 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.  \nEstablishment of prognostic models of adrenocortical carcinoma using machine learning and big data  \nJun Tang1, Yu Fang2 and Zhe Xu1*  \n1Department of Pediatric Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, 2Department of Pediatrics, China Medical University, Shenyang, China  \nBackground: Adrenocortical carcinoma (ACC) is a rare malignant tumor with a short life expectancy. It is important to identify patients at high risk so that doctors can adopt more aggressive regimens to treat their condition. Machine learning has the advantage of processing complicated data. To date, there is no research that tries to use machine learning algorithms and big data to construct prognostic models for ACC patients.  \nMethods: Clinical data of patients with ACC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. These records were screened according to preset inclusion and exclusion criteria. The remaining data were applied to univariate survival analysis to select meaningful outcome-related candidates. Backpropagation artificial neural network (BP-ANN), random forest (RF), support vector machine (SVM), and naive Bayes classifier (NBC) were chosen as alternative algorithms. The acquired cases were grouped into a training set and a test set at a ratio of 8:2, and a 10-fold cross-validation method repeated 10 times was performed. Area under the receiver operating characteristic (AUROC) curves were used as indices of efficiency.  \nResults: The calculated 1-, 3-, 5-, and 10-year overall survival rates were 62 .3%, 42. 0%, 34 .9%, and 26 . 1%, respectively. A total of 825 patients were included in the study. In the training set, the AUCs of BP-ANN, RF, SVM, and NBC for predicting 1-year survival status were 0.921, 0.885, 0.865, and 0.854; those for predicting 3-year survival status were 0.859, 0.865, 0.837, and 0.831; and those for 5-year survival status were 0.888, 0.872, 0.852, and 0.841, respectively. In the test set, AUCs of these four models for 1-year survival status were 0.899, 0.875, 0.886, and 0.862; those for 3-year survival status were 0.871, 0.858, 0.853, and 0.869; and those for 5-year survival status were 0.841, 0 .783, 0 . 836, and 0 . 867, respectively. The consequences of the 10-fold cross-validation method repeated 10 times indicated that the mean values of 1-, 3-, and 5-year AUROCs of BP-ANN were 0.890, 0.847, and 0.854, respectively, which were better than those of other classifiers (P \u003C0 . 008) . Conclusion: The model combined with BP-ANN and big data can precisely predict the survival status of ACC patients and has the potential for clinical application.  \nKEYWORDS  \nadrenocortical carcinoma, machine learning, SEER, BP-ANN, survival status","cbCaicdFGXfboRew","https://ap.wps.com/l/cbCaicdFGXfboRew","pdf",1539983,3,1,9,"English","en",105,"# Background\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Study context and rationale","[{\"question\":\"Why are prognostic models important for adrenocortical carcinoma (ACC)?\",\"answer\":\"ACC has limited treatment options and a high metastasis rate, resulting in poor prognosis. Identifying high-risk patients helps doctors select more aggressive regimens and improve outcomes.\"},{\"question\":\"How were the models trained and validated in this study?\",\"answer\":\"Patient clinical data were obtained from the SEER database, split into training and test sets in an 8:2 ratio. A 10-fold cross-validation procedure was repeated 10 times, and AUROC curves were used to assess efficiency.\"},{\"question\":\"Which machine learning algorithms were used for the prognostic models?\",\"answer\":\"The study compared BP-ANN, random forest (RF), support vector machine (SVM), and naive Bayes classifier (NBC) to predict survival status at 1, 3, 5, and 10 years.\"}]","Establishment of Prognostic Models of Adrenocortical Carcinoma - Using Machine Learning and Big Data | PDF",1785939424,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"establishment-of-prognostic-models-of-adrenocortical-carcinoma-using-machine-learning-and-big-data","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"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":20},"https://docshare.wps.com/document/healthcare/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/establishment-of-prognostic-models-of-adrenocortical-carcinoma-using-machine-learning-and-big-data/127486/",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","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are prognostic models important for adrenocortical carcinoma (ACC)?","Question",{"text":76,"@type":77},"ACC has limited treatment options and a high metastasis rate, resulting in poor prognosis. Identifying high-risk patients helps doctors select more aggressive regimens and improve outcomes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the models trained and validated in this study?",{"text":81,"@type":77},"Patient clinical data were obtained from the SEER database, split into training and test sets in an 8:2 ratio. A 10-fold cross-validation procedure was repeated 10 times, and AUROC curves were used to assess efficiency.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning algorithms were used for the prognostic models?",{"text":85,"@type":77},"The study compared BP-ANN, random forest (RF), support vector machine (SVM), and naive Bayes classifier (NBC) to predict survival status at 1, 3, 5, and 10 years.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]