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The document provides the corrected version of Table 1, including patient demographic characteristics for the full cohort, training set, and testing set, and confirms that the original article has been corrected. It also includes author and licensing information.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/correction-ct-radiomics-based-explainable-machine-learning-model-for-accurate-differentiation-of-malignant-and-benign-endometrial-tumors-a-two-center-study-table-1-corrected/460059/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/correction-ct-radiomics-based-explainable-machine-learning-model-for-accurate-differentiation-of-malignant-and-benign-endometrial-tumors-a-two-center-study-table-1-corrected/460059.png","ImageObject",300,407,{"name":42,"@type":43},"Aldword","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-08","2026-09-30",true,{"@type":52,"interactionType":53,"userInteractionCount":26},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What error is corrected in the document?","Question",{"text":62,"@type":63},"The correction states that the Gender value for Female in the third column of Table 1 was incorrect.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"What is the corrected value for Female in Table 1?",{"text":67,"@type":63},"The Female value should be 83.",{"name":69,"@type":60,"acceptedAnswer":70},"Which parts of Table 1 are included in the correction?",{"text":71,"@type":63},"Table 1 provides patient demographic characteristics across the full cohort, training set, and testing set, including age, gender, and diagnosis type.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},460059,1790757622,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},7,40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":106,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":26,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":26,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":79,"read_time":96,"preview_page_count":4},2336478940917,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Zhang et al. BioMedical Engineering OnLine (2026) 25:2  \n[https://doi.org/10.1](https://doi.org/10.1) 186/s12938‑025‑01488‑0  \nBioMedical Engineering OnLine  \nCORRECTION Open Access  \nCorrection: CT radiomics‑based explainable   machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two‑center study  \nTingrui Zhang1,2†, Honglin Wu3†, Zekun Jiang4, Yingying Wang5, Rui Ye6, Huiming Ni1, Chang Liu4, Jin Cao4, Xuan Sun1, Rong Shao7, Xiaorong Wei1 andYingchun Sun1*  \n\n| †Tingrui Zhang and Honglin Wu contributed equally to this work and share first authorship. |\n| --- |\n| The original article can be found online at [https://doi.org/10.1186/](https://doi.org/10.1186/)[ ](https://doi.org/10.1186/)[s12938-025-01462-w](s12938-025-01462-w.)[.](s12938-025-01462-w.) |\n| *Correspondence: [sunny8223@126.com](sunny8223@126.com) |\n\n1 Gynecology Department, Qingdao Traditional Chinese Medicine Hospital, Qingdao Hiser Hospital Affiliated of Qingdao University, Qingdao 266000, China  \nFull list of author information is available at the end of the article  \nCorrection: BioMedical Engineering OnLine (2025) 24:129  \n[https://doi.org/10.1186/s12938-025-01462-w](https://doi.org/10.1186/s12938-025-01462-w)  \nIn Table 1 of this article [1], Gender value for Female in the third column were incorrect and should have been 83.  \nThe corrected version of Table 1 is given in this correction.  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/.)[.](http://creativecommons.org/licenses/by-nc-nd/4.0/.)  \nZhang et al. BioMedical Engineering OnLine (2026) 25:2 Page 2 of 2  \nTable 1 Patient demographic characteristics  \n\n| Characteristics | Groups | Full cohort (n = 83) | Training set (n = 59) | Testing set (n = 24) | P |\n| --- | --- | --- | --- | --- | --- |\n| Age (years) | Mean ± | 57.46 | 56.66 | 59.42 | 0.218 |\n| Gender | SD Male Female | 8.99\u003Cbr>0\u003Cbr>83 | 8.86\u003Cbr>0\u003Cbr>59 | 9.20\u003Cbr>0\u003Cbr>24 | 1.000 |\n| Diagnosis type | Benign Malignant | 37\u003Cbr>46 | 26\u003Cbr>33 | 11\u003Cbr>13 | 0.886 |\n\nThe original article has been corrected.  \nAuthor details  \n1Gynecology Department, Qingdao Traditional Chinese Medicine Hospital, Qingdao Hiser Hospital Affiliated of Qingdao University, Qingdao 266000, China. 2Sichuan University-Pittsburgh Institute, Sichuan University, Chengdu 610000, China. 3 Department of Obstetrics and Gynecology, Qingbaijiang Women’s and Children’s Hospital (Maternal and Child Health Hospital), West China Second University Hospital, Sichuan University, Chengdu 610300, China. 4College of Computer Science, Sichuan University, Chengdu 610000, Sichuan, China. 5 Radiology Department, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao 266000, China. 6 Department of Traditional Chinese Medicine, Jiaozhou Traditional Chinese Medicine Hospital, Qingdao 266000, China. 7Adult Traditional Chinese Medicine Department, Qingdao Women and Children’s Ho","cbCaifh3Ggqnoaq4","https://ap.wps.com/l/cbCaifh3Ggqnoaq4","pdf",644007,"English","# Correction Notice\n## Corrected Table 1\n## Patient Demographic Characteristics\n## Author Details and Licensing","[{\"question\":\"What error is corrected in the document?\",\"answer\":\"The correction states that the Gender value for Female in the third column of Table 1 was incorrect.\"},{\"question\":\"What is the corrected value for Female in Table 1?\",\"answer\":\"The Female value should be 83.\"},{\"question\":\"Which parts of Table 1 are included in the correction?\",\"answer\":\"Table 1 provides patient demographic characteristics across the full cohort, training set, and testing set, including age, gender, and diagnosis type.\"}]","Correction - CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study - Table 1 corrected | PDF"]