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Updated: Aug 24, 2025

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Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer

Chan Xu1, Qian Zhou2, Wencai Liu3

  • 1Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China.

Journal of Oncology
|October 24, 2022
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Summary

This study developed a machine learning model to predict lung metastasis in kidney cancer patients. The XGBoost model demonstrated high accuracy and is available as a web calculator for clinical use.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Renal cell carcinoma (kidney cancer) poses a significant health challenge, with lung metastasis being a common and critical complication.
  • Accurate prediction of lung metastasis is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To develop and validate a clinical prediction model for lung metastasis in renal cancer patients.
  • To identify independent risk and protective factors associated with lung metastasis.
  • To leverage machine learning algorithms for robust prediction model development.

Main Methods:

  • Utilized data from the SEER database (2010-2017) for 42,650 kidney cancer patients.
  • Employed LASSO and multivariate logistic regression to identify key predictive variables.
  • Developed and trained prediction models using various machine learning algorithms (RF, NBC, DT, XGB, GBM, LR) with 10-fold cross-validation.
  • Evaluated model performance using ROC curves, probability density functions, and clinical utility curves.

Main Results:

  • Lung metastasis was observed in 7.43% of the study population.
  • Identified bone metastasis, brain metastasis, grade, liver metastasis, N stage, T stage, and tumor size as independent risk factors.
  • Primary site and sequence number were identified as independent protective factors.
  • The XGBoost (XGB) model achieved the highest performance, with AUC ranging from 0.879-0.922 in ROC analysis.
  • A web-based calculator was created based on the XGB model.

Conclusions:

  • Machine learning algorithms can effectively predict lung metastasis in kidney cancer patients.
  • The developed XGBoost model offers a low-cost, non-invasive, and easily implementable diagnostic tool for clinical settings.
  • Further real-world validation is necessary to confirm the model's broader applicability.