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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Time-dependent diffusion MRI-based microstructural mapping for predicting IDH mutation status in glioma: A

Wanjun Hu1, Wentao Liu2, Darui Li1

  • 1Department of Nuclear Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou 730000, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730000, China.

Magnetic Resonance Imaging
|December 21, 2025
PubMed
Summary
This summary is machine-generated.

Time-dependent diffusion MRI histogram features accurately predict IDH mutation status in gliomas. This multicenter study highlights the potential of advanced diffusion MRI metrics for non-invasive glioma subtyping.

Keywords:
GliomaIDH mutation statusTd-dMRI

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Area of Science:

  • Neuroimaging
  • Radiology
  • Oncology

Background:

  • Distinguishing IDH-wildtype from IDH-mutant gliomas is crucial for glioma diagnosis and treatment planning.
  • Conventional MRI has limitations in accurately differentiating these subtypes.
  • Advanced diffusion MRI techniques offer potential for improved microstructural characterization.

Purpose of the Study:

  • To assess the efficacy of time-dependent diffusion MRI (td-dMRI)-based microstructural histogram parameters in differentiating IDH mutation status in gliomas.
  • To validate the predictive model across multiple centers.

Main Methods:

  • Prospective dual-center study involving patients with pathologically confirmed gliomas.
  • td-dMRI acquisition and analysis using the IMPULSED model to derive microstructural parameters (e.g., intracellular volume fraction, cellularity).
  • Histogram feature extraction and selection, followed by logistic regression modeling for IDH mutation status prediction, with external validation.

Main Results:

  • Eight nonredundant histogram features from td-dMRI significantly differentiated IDH-wildtype and IDH-mutant gliomas.
  • A predictive model using ADCPGSE_firstorder_Energy and cellularity_firstorder_10Percentile achieved AUCs of 0.801 (training) and 0.771 (validation).
  • ADCPGSE and cellularity showed positive correlation with the Ki-67 index.

Conclusions:

  • Histogram features derived from td-dMRI, specifically ADCPGSE and cellularity, provide robust prediction of IDH mutation status in gliomas.
  • This approach demonstrates potential for non-invasive glioma subtyping in a multicenter setting.