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Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data.

Wei Jia1,2, Hailong Li1,3, Redha Ali1

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ComBat harmonization effectively removes non-biological variations in multi-center abdominal MRI data. This AI-driven technique ensures consistency in radiomic and deep features across different scanners and field strengths.

Keywords:
Artificial intelligenceComBatDeep learningHarmonizationMagnetic resonance imagingMulti-center studyRadiomics

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Data Science

Background:

  • Multi-center studies generate data with non-biological variations.
  • Artificial intelligence (AI) in medical imaging requires harmonized data.
  • Chronic liver disease research benefits from consistent abdominal MRI data.

Purpose of the Study:

  • To evaluate ComBat harmonization's effectiveness on radiomic and deep features.
  • To assess ComBat's ability to remove variations from different MRI manufacturers and field strengths.
  • To improve data consistency for AI applications in multi-center abdominal MRI studies.

Main Methods:

  • Retrospective analysis of T2-weighted abdominal MRI data from three sites.
  • Extraction of radiomic and deep features using PyRadiomics and Swin Transformer.
  • Application of ComBat harmonization to address variations across manufacturers and field strengths.
  • Statistical analysis (t-test, ANOVA, Cohen's F) to compare features before and after harmonization.

Main Results:

  • Significant differences in features existed across manufacturers and field strengths before harmonization.
  • ComBat harmonization eliminated significant differences in radiomic and deep features.
  • Reduced Cohen's F scores indicated successful harmonization.
  • ComBat effectively removed non-biological variations in large-scale abdominal MRI datasets.

Conclusions:

  • ComBat harmonization is effective in standardizing radiomic and deep features.
  • This method mitigates variations caused by different MRI systems and acquisition parameters.
  • ComBat enhances data quality for AI-driven analysis in multi-center abdominal MRI research.