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Related Experiment Video

Updated: May 27, 2025

A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells
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Simplifying Radiomics Workflow for Predicting Grade of Glioma: An Approach for Rapid and Reproducible Radiomics.

Yunus Soleymani1,2, Peyman Sheikhzadeh3, Mohammad Mohammadzadeh4

  • 1Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.

Journal of Biomedical Physics & Engineering
|February 20, 2025
PubMed
Summary
This summary is machine-generated.

Single-sequence MRI radiomics with a single region of interest (ROI) accurately grades glioma. This approach enhances segmentation reproducibility and simplifies the radiomics workflow for improved glioma classification.

Keywords:
GliomaMachine LearningMagnetic Resonance ImagingRadiomicsRadiomics Reproducibility

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Area of Science:

  • Medical Imaging
  • Oncology
  • Radiomics

Background:

  • Radiomics analysis in glioma grading can be complex due to multi-sequence MRI and intricate region delineation.
  • Simplifying the process using single-sequence MRI and a single Region of Interest (ROI) may improve workflow efficiency and reproducibility.

Purpose of the Study:

  • To assess the efficacy of radiomics for glioma grading using a single-ROI delineation on contrast-enhanced T1-weighted (CE T1W) MRI.
  • To evaluate the impact of single-ROI segmentation on radiomics reproducibility.

Main Methods:

  • Retrospective analysis of CE T1W MRI from 120 glioma patients (60 grade II, 60 grade III).
  • Manual delineation of Gross Tumor Volume (GTV) as the single ROI.
  • Extraction and selection of robust radiomics features, assessing reproducibility via Intraclass Correlation Coefficient (ICC).
  • Classification of glioma grades using a linear Support Vector Machine (SVM).

Main Results:

  • Four significant radiomics features (P-value<0.05) were identified.
  • High segmentation reproducibility was confirmed with an average ICC of 0.96.
  • The linear SVM model achieved an Area Under the Curve (AUC) of 0.9 for differentiating glioma grades in the training set.

Conclusions:

  • Radiomics analysis on single-sequence MRI (CE T1W) with single-ROI segmentation demonstrates high predictive power for glioma grading.
  • Single-ROI segmentation enhances the reproducibility of radiomics analysis, simplifying the workflow.