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Related Experiment Video

Updated: Mar 8, 2026

Translational Orthotopic Models of Glioblastoma Multiforme
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Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features.

Adrian Ion-Mărgineanu1, Sofie Van Cauter2, Diana M Sima1

  • 1Department of Electrical Engineering (ESAT), Signal Processing and Data Analytics, STADIUS Center for Dynamical Systems, KU LeuvenLeuven, Belgium; imecLeuven, Belgium.

Frontiers in Neuroscience
|January 27, 2017
PubMed
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This summary is machine-generated.

This study uses multi-parametric MRI data to differentiate glioblastoma tumor progression from treatment response in patients. Advanced MRI techniques combined with machine learning classifiers accurately predict treatment outcomes.

Area of Science:

  • Radiology
  • Oncology
  • Machine Learning

Background:

  • Glioblastoma multiforme (GBM) presents challenges in distinguishing tumor progression from treatment response using standard imaging.
  • Multi-parametric MRI (mpMRI) offers enhanced tissue characterization beyond conventional MRI (cMRI).

Purpose of the Study:

  • To discriminate between tumor progression and treatment response in GBM patients using follow-up mpMRI data.
  • To evaluate the efficacy of advanced MRI techniques (PWI, DKI) combined with machine learning for outcome prediction.

Main Methods:

  • Acquired mpMRI data (cMRI, PWI, DKI) from 29 GBM patients undergoing adjuvant therapy.
  • Developed an automated pipeline for feature extraction (histogram, texture) from manually and semi-manually delineated regions of interest (ROIs).
Keywords:
boosting classifiersfollow-upglioblastoma multiformemagnetic resonance imagingmulti-parametric

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  • Trained classifiers using a leave-one-patient-out cross-validation scheme and compared performance using Balanced Accuracy Rate (BAR).

    • Main Results:

    • Maximum BAR-values achieved were 0.956 (cMRI, manual), 0.894 (PWI, semi-manual), and 0.947 (combined modalities, semi-manual).
    • The RUSBoost classifier trained on semi-manual delineations of cMRI or combined modalities performed best.
    • AdaBoost, SVM-rbf, and RUSBoost demonstrated high accuracy in differentiating progressive from responsive GBM patients using T1pc and CBV features.

    Conclusions:

    • Multi-parametric MRI features, particularly T1 post-contrast (T1pc) and Cerebral Blood Volume (CBV), are highly effective for differentiating GBM progression from treatment response.
    • Machine learning classifiers (AdaBoost, SVM-rbf, RUSBoost) trained on these features achieve excellent accuracy.
    • Semi-manual delineations combined with advanced MRI modalities show significant potential for accurate outcome prediction in GBM.