Development of the glioma imaging complexity score (GICS): a volumetric MRI-based stratification framework

Area of Science:

• Neuro-oncology
• Radiology
• Medical Imaging Analysis

Background:

• Quantitative MRI analysis and radiomics improve neuro-oncological research reproducibility.
• Standardized quantitative frameworks for glioma MRI complexity are limited.

Purpose of the Study:

• Establish a quantitative framework for glioma imaging complexity stratification using MRI data.
• Utilize standardized imaging features from the Brain Tumor Segmentation (BraTS) initiative.

Main Methods:

• Retrospective analysis of 1,251 glioma cases from the BraTS repository.
• Extracted quantitative imaging variables (e.g., tumor volume, edema, diameter) using voxel-based segmentation.
• Categorized cases into low-, moderate-, and high-complexity groups (GICS-1, GICS-2, GICS-3) based on total tumor volume.

Main Results:

• Mean total tumor volume significantly increased across GICS categories (GICS-1 to GICS-3, p < 0.001).
• Higher GICS categories correlated with greater edema, larger tumor diameter, and increased enhancing/necrotic components.
• Strong positive correlations found between total tumor volume and maximum tumor diameter (r=0.764) and edema volume (r=0.861).

Conclusions:

• The GICS framework provides a preliminary quantitative MRI stratification model for gliomas.
• Segmentation-derived MRI variables can form reproducible stratification groups.
• This framework supports future studies on imaging-based complexity assessment and visualization in glioma surgery.