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Related Experiment Video

Updated: Jan 11, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Radiomics-based machine learning for glioma grade classification: a multicenter study with SHapley Additive

Yumeng Shi1,2, Wei Jiang3, Li Zhou1,2

  • 1Department of Radiotherapy, Affiliated Hospital of Nantong University, Nantong, China.

Translational Cancer Research
|November 14, 2025
PubMed
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This study developed an interpretable machine learning model using MRI radiomics to accurately predict glioma World Health Organization (WHO) grade. The model shows promise for clinical use in guiding treatment and prognosis.

Area of Science:

  • Neuroradiology
  • Machine Learning in Oncology
  • Medical Imaging Analysis

Background:

  • Accurate preoperative grading of gliomas is crucial for treatment planning and prognosis.
  • Current methods for glioma grading can be limited.
  • This study leverages advanced machine learning on MRI data to improve glioma grading.

Purpose of the Study:

  • To develop and validate a robust, interpretable machine learning (ML) model for predicting glioma World Health Organization (WHO) grade.
  • To utilize multicenter magnetic resonance imaging (MRI) radiomics data for enhanced predictive accuracy.
  • To compare the performance of various ML models for glioma grading.

Main Methods:

  • Collected preoperative MRI data from 905 glioma patients across three independent cohorts.
Keywords:
GliomaWorld Health Organization grading (WHO grading)interpretable modelmachine learning (ML)radiomics

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  • Extracted radiomics features from T1-weighted contrast-enhanced MRI scans.
  • Developed and validated an extreme gradient boosting (XGBoost) model, interpreted using SHapley Additive exPlanations (SHAP).

    • Main Results:

    • The XGBoost model achieved high performance with AUCs ranging from 0.834 to 0.983 across training and validation sets.
    • The model demonstrated accuracy of 82.3-94.3%, outperforming conventional imaging assessment.
    • SHAP analysis identified texture heterogeneity features as key predictors of glioma grade.

    Conclusions:

    • A robust and interpretable radiomics-based ML model for preoperative glioma WHO grade prediction was successfully developed and validated.
    • The model's strong performance across diverse datasets indicates potential for clinical translation.
    • Further prospective validation is needed to confirm clinical utility and impact on patient outcomes.