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Predicting distant metastasis in early-onset kidney cancer using machine learning: a SEER database study with

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Summary

Machine learning accurately predicts distant metastasis in early-onset kidney cancer (EOKC). The Gradient Boosting Decision Tree (GBDT) model identifies tumor size and grade as key risk factors, aiding clinical decisions.

Keywords:
Distant metastasisEarly-onset kidney cancerMachine learningPredictive modelSEER

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Area of Science:

  • Oncology
  • Medical Informatics
  • Machine Learning

Background:

  • Early-onset kidney cancer (EOKC) patients have a poor prognosis post-metastasis.
  • Current methods for predicting metastasis in EOKC lack precision.
  • Accurate prediction is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To develop and validate a machine learning-based predictive model for distant metastasis in EOKC.
  • To identify key risk factors associated with metastasis in EOKC.
  • To provide a tool for enhanced clinical decision-making in EOKC management.

Main Methods:

  • Utilized the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015) and external validation cohorts.
  • Applied Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression for variable selection.
  • Constructed and evaluated multiple machine learning models, including Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN).
  • Assessed model performance using accuracy, precision, F1 score, Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA).
  • Employed SHapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

  • The Gradient Boosting Decision Tree (GBDT) model demonstrated superior performance across training, internal, and external validation cohorts, with high AUC values (e.g., 0.940 in training).
  • Tumor T stage, N stage, pathological grade, and tumor size were identified as independent risk factors for distant metastasis.
  • SHAP analysis confirmed tumor size and grade as the most significant predictive features.
  • The GBDT model showed strong calibration and clinical utility via DCA.

Conclusions:

  • A robust and interpretable machine learning model (GBDT) for predicting distant metastasis in EOKC has been developed and validated.
  • The model effectively utilizes key clinical and pathological features to predict metastasis risk.
  • This tool can support personalized treatment strategies and improve clinical decision-making for EOKC patients.