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Related Experiment Video

Updated: Jan 15, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Ensemble machine learning models for predicting bone metastasis in bladder cancer.

Zhan Jiang Yu1, Xiang Da Xu1, Xin Chang Zou1

  • 1The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.

Frontiers in Oncology
|October 13, 2025
PubMed
Summary

Machine learning models accurately predict bone metastasis in bladder cancer (BC). The Gradient Boosting Machine (GBM) model shows high performance, aiding personalized treatment and screening for bladder cancer bone metastasis (BCBM).

Keywords:
SEER databasebladder cancerbone metastasismachine learningpredictive value

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Area of Science:

  • Oncology
  • Medical Informatics
  • Machine Learning

Background:

  • Bone metastasis (BM) in advanced bladder cancer (BC) is a significant prognostic indicator.
  • Accurate prediction of BM in BC is currently challenging, impacting patient outcomes.
  • Developing reliable predictive tools is crucial for personalized clinical management.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting bladder cancer bone metastasis (BCBM).
  • To identify independent risk factors associated with BCBM.
  • To enhance clinical decision-making and screening efficiency for BCBM.

Main Methods:

  • Utilized data from the SEER database (2010-2015) and an external validation cohort.
  • Identified risk factors using univariate and multivariate logistic regression.
  • Developed and compared seven machine learning models: LR, SVM, GBM, NN, RF, XGB, and KNN.
  • Evaluated model performance using AUC, accuracy, sensitivity, and specificity.

Main Results:

  • A total of 22,114 BC patients were analyzed, with 2.4% developing BM.
  • Identified risk factors include age, race, tumor stage (T, N), histology, grade, and metastasis to other organs.
  • The Gradient Boosting Machine (GBM) model achieved the highest performance (AUC 0.855, accuracy 0.813) in the test set.
  • The GBM model demonstrated strong performance in the external validation set (AUC 0.766, accuracy 0.945).
  • T stage, N stage, and radiotherapy history were the most significant predictors in the GBM model.

Conclusions:

  • The developed GBM model provides a precise and personalized method for predicting BCBM.
  • This predictive tool can potentially improve clinical decision-making for bladder cancer patients.
  • Enhanced prediction of BCBM can lead to more efficient screening strategies and better patient management.