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Four machine learning models (XGBoost, Random Forest, SVM, KNN) were evaluated with ROC, calibration, DCA, and k-fold cross-validation, and key drivers were interpreted using SHAP.",{"@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/research-report/","Research & 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was the main goal of the study?","Question",{"text":62,"@type":63},"To develop a recurrence risk prediction model for bladder cancer using clinical characteristics, laboratory indicators, and postoperative follow-up data, and to identify key recurrence risk factors.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Which machine learning algorithms were compared?",{"text":67,"@type":63},"The study constructed predictive models using XGBoost, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN).",{"name":69,"@type":60,"acceptedAnswer":70},"What factors were identified as independent predictors of recurrence?",{"text":71,"@type":63},"Age, smoking history, tumor stage, tumor number, tumor size, pathological grade, neutrophil-to-lymphocyte ratio (NLR), urine cytology, hematuria, NMP22, and alkaline phosphatase (ALP) were reported as independent 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postoperative recurrence in bladder cancer using clinical and laboratory indicators  \nLanyu Wang1,3,7, Ju Zhang2,7, Yuan Liu4,7, Yifan Sun3, Ye Hua5, Xiang Zhang6, Ninghan Feng1,3,6􀀍, Jianfeng Shao2,3􀀍 & Chunyang Chen2􀀍  \nPostoperative recurrence is a major determinant of prognosis in bladder cancer. Early identification of patients at high risk is essential for optimizing individualized follow-up and therapeutic strategies. This study aimed to develop a comprehensive recurrence risk prediction model based on clinical characteristics, laboratory parameters, and postoperative follow-up data, and to identify the key risk factors associated with recurrence. A total of 488 patients with bladder cancer were retrospectively enrolled. Demographic, lifestyle, comorbidity, tumor-related, surgical, and laboratory data at 3 months postoperatively were collected. Univariate and multivariate analyses were conducted to identify recurrence-associated variables. Predictive models were constructed using four machine learning algorithms: eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN). Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and k-fold cross-validation. Feature importance and individual risk contributions were interpreted using SHAP (SHapley Additive exPlanations) analysis. Age, smoking history, tumor stage, tumor number, tumor size, pathological grade, neutrophil-to-lymphocyte ratio (NLR), urine cytology, hematuria, NMP22, and alkaline phosphatase (ALP) were identified as independent predictors of bladder cancer recurrence. Among all models, XGBoost demonstrated the best predictive performance, with anAUC of 0.960 in the training set, 0.925 in the validation set, and 0.850 in the external validation cohort.  \nSHAP analysis revealed that smoking history, tumor stage, tumor number, tumor size, pathological grade, NLR, urine cytology, hematuria, and NMP22 were the most influential predictors of recurrence and contributed significantly to inter-individual risk differences. The multidimensional machine learning–based recurrence prediction model developed in this study accurately identifies high-risk bladder cancer patients and elucidates key risk factors, offering a robust evidence base for personalized postoperative surveillance and intervention. Furthermore, it provides novel insights into the biological mechanisms underlying recurrence. Future studies with larger, multicenter cohorts are warranted to validate the model’s robustness and clinical applicability.  \nKeywords XGBoost, Bladder cancer, Recurrence, Machine learning, Inflammatory markers  \nBladder cancer is one of the most common malignant tumors of the urinary system, with more than 600,000 new cases reported worldwide in 2022, accounting for approximately 3% of all newly diagnosed cancers. It is particularly prevalent in men and shows a steadily increasing incidence trend. Clinically, bladder cancer is primarily classified into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC)1–3. Although NMIBC generally has a more favorable prognosis compared to MIBC, the postoperative  \n1Department of Urology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. 2Department of Urology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China. 3Department of Urology, Jiangnan University Medical Center, Wuxi, China. 4Department of General Surgery, Tengzhou Central People’s Hospital, Jining Medical College, Shandong, China. 5Department of Neurology, Jiangnan University Medical Center, Wuxi, China. 6Department of Urology, Nantong University, Wuxi No.2 Hospital, W","cbCaiqkVT7fGPpFr","https://ap.wps.com/l/cbCaiqkVT7fGPpFr","pdf",2737955,16,"English","# Background\n## Clinical need for recurrence prediction\n## Limitations of existing tools\n# Methods\n## Patient cohort and data collection\n## Machine learning models\n## Model evaluation and interpretation\n# Results\n## Independent predictors of recurrence\n## Model performance comparison\n## SHAP feature importance findings\n# Discussion and Implications\n## Personalized surveillance and intervention\n## Biological insights and future validation","[{\"question\":\"What was the main goal of the study?\",\"answer\":\"To develop a recurrence risk prediction model for bladder cancer using clinical characteristics, laboratory indicators, and postoperative follow-up data, and to identify key recurrence risk factors.\"},{\"question\":\"Which machine learning algorithms were compared?\",\"answer\":\"The study constructed predictive models using XGBoost, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN).\"},{\"question\":\"What factors were identified as independent predictors of recurrence?\",\"answer\":\"Age, smoking history, tumor stage, tumor number, tumor size, pathological grade, neutrophil-to-lymphocyte ratio (NLR), urine cytology, hematuria, NMP22, and alkaline phosphatase (ALP) were reported as independent predictors.\"}]","Machine learning prediction of postoperative recurrence in bladder cancer using clinical and laboratory indicators | PDF",1790058058]