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Related Experiment Video

Updated: May 4, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain

Huai-Wen Zhang1, Yi-Ren Wang2,3, Bo Hu4

  • 1Department of Radiotherapy, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, Nanchang, 330029, China. 1761580890@qq.com.

Scientific Reports
|November 20, 2024
PubMed
Summary

Machine learning accurately classifies brain metastases using radiomic features. A stacking ensemble model combining multiple algorithms outperformed individual models, improving tumor volume assessment for radiotherapy.

Keywords:
Artificial intelligenceMachine learningPrediction modelRadiomicsStacking ensemble learning

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Area of Science:

  • Radiomics
  • Machine Learning in Medical Imaging
  • Oncology

Background:

  • Accurate assessment of tumor volume is crucial for radiotherapy planning in patients with brain metastases.
  • Traditional methods for identifying and classifying brain metastases can be time-consuming and may lack precision.
  • Machine learning (ML) offers potential for automated analysis of medical images, including radiomic feature extraction.

Purpose of the Study:

  • To explore the efficacy of ML techniques for automatic identification and classification of brain metastases from a radiomic perspective.
  • To enhance the accuracy of tumor volume assessment for radiotherapy planning.
  • To develop and evaluate a stacking ensemble model integrating multiple ML algorithms for improved classification performance.

Main Methods:

  • Utilized nine ML algorithms: random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost.
  • Developed a stacking ensemble model to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features.
  • Assessed model performance using metrics including specificity, sensitivity, accuracy, and area under the curve (AUC).

Main Results:

  • The stacking ensemble model achieved high performance in classifying GTV (AUC=0.928), brainstem (AUC=0.932), and normal brain tissue (AUC=0.942).
  • The support vector machine (SVM) model showed the best performance among individual base models (AUCs ranging from 0.909 to 0.928).
  • The ensemble model consistently outperformed individual models, demonstrating the benefit of integrating diverse algorithms, especially in high-dimensional spaces where some models like decision trees and k-nearest neighbors showed lower performance.

Conclusions:

  • A stacking ensemble ML model effectively classifies brain metastases and surrounding tissues using radiomic features.
  • Combining multiple ML algorithms in an ensemble approach yields superior results compared to individual models for this classification task.
  • This approach shows significant promise for improving tumor volume assessment in radiotherapy for brain metastases.