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Related Experiment Video

Updated: Nov 7, 2025

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Published on: April 12, 2020

Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance

Sirui Li¹, Yuan Zheng², Wenbo Sun¹

¹Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.

European Radiology

|April 29, 2021

Summary

This summary is machine-generated.

Diffusional variance decomposition (DIVIDE) shows promise for glioma diagnosis. This advanced imaging technique can differentiate tumor grades and molecular subtypes, correlating with cell density and malignancy.

Keywords:

Classification Diffusion magnetic resonance imaging Glioma Isocitrate dehydrogenase Neuroimaging

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Area of Science:

Neuroimaging
Radiology
Oncology

Background:

Gliomas are primary brain tumors with varying grades and molecular subtypes.
Accurate grading and molecular classification are crucial for treatment planning and prognosis.
Advanced MRI techniques are needed for non-invasive glioma characterization.

Purpose of the Study:

To assess the utility of diffusional variance decomposition (DIVIDE) for glioma grading.
To evaluate DIVIDE's potential in classifying molecular features, including IDH mutation status.
To characterize glioma microstructural properties using DIVIDE-derived metrics.

Main Methods:

Suspected glioma patients underwent DIVIDE imaging, generating maps for FA, MD, MK_A, MK_I, MK_T, MK_A/MK_T, and μFA.
Tumor grade, IDH status, and Ki-67 labeling index (LI) were determined post-surgery.
Statistical analyses, including ROC and Spearman correlation, compared DIVIDE metrics between glioma groups.

Main Results:

All DIVIDE metrics significantly differed between high-grade gliomas (HGG) and low-grade gliomas (LGG), and across grades II, III, and IV.
DIVIDE metrics effectively distinguished between IDH-mutant and wild-type gliomas.
MK_T and FA showed the highest accuracy in differentiating HGG from LGG and IDH status, respectively. Most metrics correlated with Ki-67 LI, with MK_I showing the strongest correlation.

Conclusions:

DIVIDE is a valuable tool for glioma characterization, offering insights into microstructural properties.
DIVIDE metrics can effectively differentiate glioma grades and IDH mutation status.
DIVIDE shows potential for non-invasive glioma diagnosis and molecular subtyping.