AUTHOR=Yu Zhan Jiang , Xu Xiang Da , Zou Xin Chang , Su Pei De , Chao Hai Chao , Zeng Tao 

TITLE=Ensemble machine learning models for predicting bone metastasis in bladder cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1653506

DOI=10.3389/fonc.2025.1653506

ISSN=2234-943X

ABSTRACT=Background and purposeThe occurrence of bone metastasis (BM) in advanced bladder cancer (BC) often signifies a poor prognosis. Currently, the accurate prediction of BM in BC remains a challenge. This study develops predictive models using machine learning algorithms to predict bladder cancer bone metastasis (BCBM) and aid in personalized clinical decisions.Patients and methodsWe reviewed and analyzed data from patients diagnosed with BC between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. In addition, we included 327 patients treated at the Second Affiliated Hospital of Nanchang University and Jiangxi Cancer Hospital as an external validation cohort. Independent risk factors for BM in patients with BC were identified through univariate and multivariate logistic regression analyses. These features were then integrated into seven machine learning algorithms to build predictive models: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), neural network (NN), random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), along with accuracy, sensitivity (recall), and specificity.ResultsA total of 22,114 patients diagnosed with BC were included in this study, with 537 (2.4%) patients developing BM. The identified independent risk factors for BCBM included age, race, tumor histology, tumor grade, T stage, N stage, the presence of brain metastasis, liver metastasis, and lung metastasis, and history of radiotherapy. Among the seven developed machine learning models, the tree-based GBM model exhibited the best performance in the test set, achieving AUC, accuracy, sensitivity, and specificity values of 0.855, 0.813, 0.733, and 0.815, respectively. The GBM model also demonstrated robust performance in the external validation set, achieving an AUC of 0.766 and accuracy of 0.945. According to Shapley additive explanations (SHAP), the most significant feature in the GBM prediction model is the T stage, followed by the N stage and radiotherapy.ConclusionThe GBM model offers a precise and personalized approach to predicting BCBM, potentially enhancing clinical decision-making and the efficiency of BM screening in patients with BC.