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Explainable Machine Learning Models for Glioma Subtype Classification and Survival Prediction

  • 0Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
Cancers +

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August 28, 2025

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August 28, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Explainable machine learning models accurately classify glioma subtypes and predict patient survival using RNA-seq data. Key genes identified offer insights into tumor biology and prognosis for improved clinical decision-making.

Area Of Science

  • Oncology
  • Bioinformatics
  • Computational Biology

Background

  • Gliomas are aggressive brain tumors with poor prognoses, necessitating early and accurate diagnosis.
  • Tumor classification and survival prediction are critical for effective glioma treatment strategies.

Purpose Of The Study

  • To develop and validate explainable machine learning (ML) models for classifying glioma subtypes (astrocytoma, oligodendroglioma, glioblastoma).
  • To predict patient survival rates using RNA-sequencing (RNA-seq) data.
  • To enhance model transparency through Shapley additive explanations (SHAP) analysis.

Main Methods

  • Analysis of publicly available RNA-seq datasets.
  • Application of feature selection to identify key gene biomarkers.
  • Development and comparison of various ML models for classification and survival analysis.
  • Interpretation of model predictions using SHAP values.

Main Results

  • Thirteen key genes (e.g., TERT, VEGFA, MMP9) were identified as significantly associated with glioma subtypes and survival.
  • Support Vector Machine (SVM) achieved a balanced accuracy of 0.816 and AUC of 0.896 for classification.
  • Case-Control Cox regression (CoxCC) model demonstrated strong survival prediction with a C-index of 0.809.
  • SHAP analysis provided insights into gene expression's influence on model outcomes.

Conclusions

  • The developed explainable ML models offer a robust tool for glioma diagnosis and prognosis.
  • These models can assist clinicians in tailoring treatment strategies for improved patient outcomes.
  • The identified gene biomarkers hold potential for further research into glioma pathogenesis.

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