[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125460-en":3,"doc-seo-125460-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125460,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","An explainable machine learning model for predicting bladder tumor aecurrence risk - Research summary","Background: Bladder cancer is characterized by substantial postoperative recurrence, and accurate risk prediction remains difficult in routine care. Objective: Develop and validate an explainable machine learning model to predict bladder tumor recurrence after surgical treatment. Methods: A retrospective cohort of 504 patients (2018–2024) was split into training and testing sets; LASSO selected 19 candidate features and 11 algorithms were evaluated with multiple performance metrics. Results: An XGBoost model using seven features achieved AUC 0.994 and SHAP-based feature contributions. Conclusion: The interpretable approach supports clinician risk stratification and individualized surveillance planning.","TYPE Original Research PUBLISHED 29 January 2026 DOI 10.3389/fonc.2026.1728056  \nOPEN ACCESS  \nEDITED BY  \nFrancesca Sanguedolce, University of Foggia, Italy  \nREVIEWED BY  \nLei Yang,  \nHarbin Medical University, China Weibing Shuang,  \nFirst Hospital of Shanxi Medical University, China  \nTarek Ajami,  \nHospital Clinic of Barcelona, Spain  \n*CORRESPONDENCE  \nWei Hu  \n [huweiJZMU@outlook.com](huweiJZMU@outlook.com)  \nRECEIVED 19 October 2025  \nREVISED 02 January 2026  \nACCEPTED 02 January 2026  \nPUBLISHED 29 January 2026  \nCITATION  \nWu S, Wang Y, He J, Peng W and Hu W (2026) An explainable machine learning model for predicting bladder tumor aecurrence risk.  \nFront. Oncol. 16:1728056 .  \ndoi: 10.3389/fonc.2026.1728056  \nCOPYRIGHT  \n© 2026 Wu, Wang, He, Peng and Hu. 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.  \nAn explainable machine learning model for predicting bladder tumor aecurrence risk  \nShenghua Wu 1, Ying Wang 1, Jingbing He 1, Weixing Peng 1 and Wei Hu 2*  \n1Zhejiang Dinghai Hospital (Zhoushan Branch of Shanghai Ruijin Hospital), Zhoushan, Zhejiang, China, 2School of Nursing, Jinzhou Medical University, Jinzhou, China  \nBackground: Bladder cancer is associated with considerable postoperative recurrence rates. Accurate risk prediction remains challenging in clinical practice. Objective: To develop and validate an explainable machine learning model for predicting bladder tumor recurrence following surgical treatment.  \nMethods: This retrospective cohort study enrolled 504 patients with pathologically confirmed bladder tumors treated at the Department of Urology, Zhejiang Dinghai Hospital, from October 2018 to October 2024 . Postoperative surveillance was conducted at 3, 6, 12, and 24 months to assess recurrence status. The dataset was randomly partitioned into training (n=352) and testing (n=152) sets prior to analysis. LASSO regression with lambda. 1se criterion was performed exclusively on the training set to identify predictive features, yielding 19 candidate variables. Subsequently, eleven machine learning algorithms were evaluated: Logistic Regression, Random Forest, XGBoost, Gradient Boosting Machine, Neural Network, AdaBoost, Decision Tree, C5.0, Support Vector Machine, Elastic Net, and Naive Bayes. Model performance was assessed using area under the receiver operating characteristic curve (AUC), recall, accuracy, F1-score, precision, and negative predictive value (NPV), with 95% confidence intervals calculated for all metrics.  \nResults: During follow-up, 90 of 504 patients (17 . 9%) developed tumor recurrence . XGBoost utilizing seven features demonstrated optimal performance, achieving an AUC of 0 . 994 in the independent testing set. The final predictive variables included BMI, maximum tumor diameter, tumor morphology, smoking status, extravesical invasion signs, tumor number, and dome location. SHAP analysis identified BMI (mean absolute SHAP value: 1 . 5359) and maximum tumor diameter (1.4565) as primary contributors to predictions, followed by morphology (1 .3370) and smoking status (1 . 2798) .  \nConclusion: The seven-feature XGBoost model provides accurate prediction of bladder tumor recurrence with transparent feature contributions . This explainable approach may assist clinicians in risk stratification and individualized surveillance planning.  \nKEYWORDS  \nbladder neoplasms, interpretability, LASSO regression, machine learning, neoplasm recurrence, risk assessment, XGBoost  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nBladder cancer, predominantly urothelial carcinoma represen","cbCaivjpFKI5dHLO","https://ap.wps.com/l/cbCaivjpFKI5dHLO","pdf",1902243,1,13,"English","en",105,"# Background and Objective\n## Methods\n## Results\n## Conclusion\n# Key Variables and Explainability","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets the challenge of accurately predicting bladder tumor recurrence after surgical treatment, where recurrence rates are substantial and prediction is difficult in clinical practice.\"},{\"question\":\"How was the predictive model developed and evaluated?\",\"answer\":\"A retrospective cohort of 504 patients was split into training and testing sets; LASSO regression (on the training set) selected features, then 11 machine learning algorithms were compared using AUC, recall, accuracy, F1-score, precision, and NPV with 95% confidence intervals.\"},{\"question\":\"Which model performed best and what does explainability show?\",\"answer\":\"The XGBoost model using seven features achieved the best performance with an AUC of 0.994 on the independent testing set. SHAP analysis identified BMI and maximum tumor diameter as primary contributors, followed by tumor morphology and smoking status.\"}]","An explainable machine learning model for predicting bladder tumor aecurrence risk - Research summary | PDF",1785899126,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-explainable-machine-learning-model-for-predicting-bladder-tumor-aecurrence-risk-research-summary","",{"@graph":36,"@context":85},[37,54,68],{"@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/an-explainable-machine-learning-model-for-predicting-bladder-tumor-aecurrence-risk-research-summary/125460/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"The study targets the challenge of accurately predicting bladder tumor recurrence after surgical treatment, where recurrence rates are substantial and prediction is difficult in clinical practice.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the predictive model developed and evaluated?",{"text":80,"@type":76},"A retrospective cohort of 504 patients was split into training and testing sets; LASSO regression (on the training set) selected features, then 11 machine learning algorithms were compared using AUC, recall, accuracy, F1-score, precision, and NPV with 95% confidence intervals.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what does explainability show?",{"text":84,"@type":76},"The XGBoost model using seven features achieved the best performance with an AUC of 0.994 on the independent testing set. 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