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Related Experiment Video

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Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic

Jeongwon Lee1, Seung Hong Choi, Ji-Hoon Kim

  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea; Medical Imaging Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea.

NMR in Biomedicine
|July 22, 2014
PubMed
Summary
This summary is machine-generated.

Excluding cystic or necrotic areas from tumor regions of interest improves glioma grading. This method enhances differentiation between low-grade and high-grade gliomas using apparent diffusion coefficient histogram analysis.

Keywords:
apparent diffusion coefficient mapsdiffusion-weighted MRIgliomagrade

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Area of Science:

  • Neuro-oncology
  • Radiology
  • Medical Imaging Analysis

Background:

  • Accurate glioma subtype diagnosis is crucial for treatment selection.
  • Conventional histopathology for glioma grading faces challenges with intra-observer variability and sampling errors.
  • Advanced imaging techniques are needed to improve diagnostic accuracy.

Purpose of the Study:

  • To evaluate the efficacy of histogram analysis with a segmented region of interest (ROI), excluding cystic/necrotic portions, for differentiating low-grade from high-grade gliomas.
  • To compare the diagnostic performance of this novel ROI method against traditional whole-tumor ROI analysis.
  • To assess the utility of normalized apparent diffusion coefficient (ADC) maps in this differentiation.

Main Methods:

  • Retrospective analysis of 32 patients (9 low-grade, 23 high-grade gliomas).
  • Manual delineation of entire tumor boundaries on contrast-enhanced T1-weighted MRI.
  • Automatic segmentation and exclusion of cystic or necrotic components to define the ROI.
  • Histogram analysis performed on normalized ADC maps within the defined ROI.
  • Comparison of receiver operating characteristic (ROC) curve areas to assess diagnostic performance.

Main Results:

  • The ROI method, excluding cystic/necrotic portions, demonstrated superior performance in glioma grading compared to the entire tumor ROI.
  • Specifically, the fifth percentile values of the normalized ADC histogram from the segmented ROI yielded a significantly higher area under the ROC curve (p < 0.005).
  • Automatic segmentation of non-viable tumor areas likely enhances the differentiation capability between high- and low-grade gliomas.

Conclusions:

  • Histogram analysis utilizing an automatically segmented ROI, excluding cystic and necrotic areas, significantly improves the differentiation between low- and high-grade gliomas.
  • This approach offers a more accurate and reliable method for glioma grading compared to analyzing the entire tumor volume.
  • The findings suggest that incorporating ADC map histogram analysis with targeted ROI segmentation can enhance diagnostic precision in neuro-oncology.