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Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis.

Cristiana Fiscone1, Giovanni Sighinolfi2, David Neil Manners3,4

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Quantitative Susceptibility Mapping (QSM) radiomics can identify specific signs of pre-clinical inflammation in multiple sclerosis (MS) by analyzing normal-appearing white matter. This approach offers a more precise method for detecting early disease changes in the central nervous system.

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Area of Science:

  • Neuroimaging
  • Radiology
  • Biomarker Discovery

Background:

  • Multiple sclerosis (MS) is a central nervous system demyelinating disease.
  • Conventional MRI may show non-specific lesions.
  • Quantitative MRI offers potential for early inflammation detection.

Purpose of the Study:

  • To explore Quantitative Susceptibility Mapping (QSM) radiomics for analyzing normal-appearing white matter (NAWM) in MS.
  • To quantify imaging biomarkers for pre-clinical inflammation.
  • To assess the reliability of QSM-based radiomic features.

Main Methods:

  • Utilized Quantitative Susceptibility Mapping (QSM) and radiomics.
  • Analyzed normal-appearing white matter (NAWM) and its tracts.
  • Included T1w, T2w, QSM, and DWI MRI sequences.
  • Assessed feature reliability.

Main Results:

  • QSM-based radiomic features were extracted from NAWM.
  • The study outlines a workflow for QSM radiomics analysis.
  • Feature reliability was assessed in a cohort of MS patients and healthy controls.

Conclusions:

  • QSM radiomics shows promise for identifying specific imaging biomarkers in MS.
  • This technique can potentially detect pre-clinical inflammatory changes.
  • The presented workflow and dataset facilitate further research in MS neuroimaging.