[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119753-en":3,"doc-seo-119753-105":30,"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":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},119753,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","A machine learning-based model for predicting distant metastasis in patients with rectal cancer","Early identification of patients at high risk of distant metastasis from rectal cancer is critical because distant spread is linked to poorer survival and quality of life. An XGBoost machine-learning model was developed using 23,867 rectal cancer patients from the SEER database (2010–2017) and validated externally in 1,178 patients from Chinese hospitals. Model tuning used random search and tenfold cross-validation, with performance assessed by AUC, AUPRC, calibration, decision-curve analysis, and interpretability via SHAP, producing a web calculator for clinical risk prediction.","TYPE Original Research PUBLISHED 15 August 2023 DOI 10.3389/fonc.2023.1235121  \nOPEN ACCESS  \nEDITED BY  \nAndrea Balla,  \nSan Raffaele Scientific Institute (IRCCS), Italy  \nREVIEWED BY Jiancong Hu,  \nSun Yat-sen University, China Shuyi Cen,  \nStanford University, United States  \n*CORRESPONDENCE Quan Wang  \n [wquan@jlu.edu.cn](wquan@jlu.edu.cn)  \nRECEIVED 14 June 2023  \nACCEPTED 25 July 2023  \nPUBLISHED 15 August 2023  \nCITATION  \nQiu B, Shen Z, Wu S, Qin X, Yang D and Wang Q (2023) A machine learningbased model for predicting distant metastasis in patients with rectal cancer. Front. Oncol. 13:1235121 .  \ndoi: 10.3389/fonc.2023.1235121  \nCOPYRIGHT  \n© 2023 Qiu, Shen, Wu, Qin, Yang and Wang. 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.  \nA machine learning-based model for predicting distant metastasis in patients with rectal cancer  \nBinxu Qiu 1, Zixiong Shen 2, Song Wu 1, Xinxin Qin 1, Dongliang Yang 1 and Quan Wang 1*  \n1 Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China, 2 Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China  \nBackground: Distant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential.  \nMethod: The study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precisionrecall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley’s Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer.  \nResult: The study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis . Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, peri neural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0 .859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0 . 900, and precision was 0 .880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0 .810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer.  \nConclusion: The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in  \nFrontiers in Oncology 01 [frontiersin.org]","cbCaikAbQySPw6uh","https://ap.wps.com/l/cbCaikAbQySPw6uh","pdf",4697858,1,16,"English","en",105,"# Background\n# Methods\n## Model development and validation\n## Performance evaluation and interpretability\n# Results\n# Conclusion","[{\"question\":\"Why is predicting distant metastasis in rectal cancer clinically important?\",\"answer\":\"Distant metastasis from rectal cancer is associated with worse survival and reduced quality of life. Early identification of high-risk patients supports better prognostic outcomes and clinical decision-making.\"},{\"question\":\"How was the machine-learning model developed and validated?\",\"answer\":\"The study trained eight machine-learning algorithms and developed models using 23,867 SEER patients from 2010–2017. External validation used 1,178 rectal cancer patients from Chinese hospitals, and hyperparameters were tuned with random search and tenfold cross-validation.\"},{\"question\":\"Which model performed best and how was it evaluated?\",\"answer\":\"The extreme gradient boosting (XGB) model performed best in both internal testing and external validation. Evaluation used AUC, AUPRC, calibration curves, decision curve analysis, and precision/accuracy metrics.\"}]","A machine learning-based model for predicting distant metastasis in patients with rectal cancer | PDF",1785726125,40,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-machine-learning-based-model-for-predicting-distant-metastasis-in-patients-with-rectal-cancer","",{"@graph":36,"@context":86},[37,54,69],{"@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/a-machine-learning-based-model-for-predicting-distant-metastasis-in-patients-with-rectal-cancer/119753/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",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 is predicting distant metastasis in rectal cancer clinically important?","Question",{"text":76,"@type":77},"Distant metastasis from rectal cancer is associated with worse survival and reduced quality of life. Early identification of high-risk patients supports better prognostic outcomes and clinical decision-making.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the machine-learning model developed and validated?",{"text":81,"@type":77},"The study trained eight machine-learning algorithms and developed models using 23,867 SEER patients from 2010–2017. External validation used 1,178 rectal cancer patients from Chinese hospitals, and hyperparameters were tuned with random search and tenfold cross-validation.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best and how was it evaluated?",{"text":85,"@type":77},"The extreme gradient boosting (XGB) model performed best in both internal testing and external validation. 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