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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter.

Yuting Yan1, Xiaodong He1, Yuyun Xu1

  • 1Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.

Frontiers in Aging Neuroscience
|April 30, 2024
PubMed
Summary
This summary is machine-generated.

The radiomics model outperformed traditional voxel-based morphometry (VBM) and surface-based morphometry (SBM) in diagnosing brain aging. Radiomics offers superior performance and generalization, while VBM-SBM is better for interpretability.

Keywords:
grey mattermagnetic resonance imagingmorphometrynormal agingradiomics

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Area of Science:

  • Neuroimage analysis
  • Brain aging research
  • Medical imaging biomarkers

Background:

  • Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are established neuroimaging techniques.
  • Radiomics is an emerging field in neuroimage analysis.
  • Direct performance comparisons between VBM-SBM and radiomics for brain aging diagnosis are limited.

Purpose of the Study:

  • To develop and compare VBM-SBM and radiomics models for diagnosing brain aging in cognitively normal individuals.
  • To evaluate the strengths, weaknesses, and relationships of these neuroimaging approaches.
  • To determine the optimal method for identifying age-related brain changes.

Main Methods:

  • 967 cognitively normal participants were categorized into middle-aged (n=302) and old-aged (n=665) groups.
  • VBM-SBM and radiomics models were trained and tested using data from ADNI, AIBL, NACC, and PPMI databases.
  • Logistic regression was used for model construction, with performance evaluated by AUC, sensitivity, specificity, and accuracy.

Main Results:

  • The radiomics model demonstrated superior performance across training and multiple external test datasets compared to the VBM-SBM model.
  • Area Under the Curve (AUC) values for the radiomics model were consistently higher than for the VBM-SBM model in most test sets.
  • Weak correlations were found between VBM-SBM parameters and radiomics features, suggesting complementary information.

Conclusions:

  • The radiomics model shows greater performance and generalization capabilities for diagnosing brain aging.
  • VBM-SBM models are more suitable when interpretability and direct clinical application are prioritized.
  • Radiomics presents a promising avenue for advanced neuroimage analysis in aging research.