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Differentiating low from high-grade soft tissue sarcomas using post-processed imaging parameters derived from

Georgios C Manikis1, Katerina Nikiforaki1, Eleni Lagoudaki2

  • 1Department of Radiology, Medical School-University of Crete, Heraklion, Greece; Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece.

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|March 23, 2021
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Summary

Model selection in diffusion parametric maps improves the grading of soft tissue sarcomas (STSs). This approach enhances the discriminatory power of histogram metrics derived from meta-maps, achieving high accuracy in differentiating tumor grades.

Keywords:
Diffusion weighted imagingHistogram analysisHybrid parametric mapsModel selectionQuantitative MRISoft tissue sarcomas

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Area of Science:

  • Radiology
  • Medical Imaging
  • Oncology

Background:

  • Soft tissue sarcomas (STSs) require accurate grading for effective treatment.
  • Diffusion Weighted Imaging (DWI) offers insights into tissue microstructure.
  • Current DWI models may not fully capture the heterogeneity of STSs.

Purpose of the Study:

  • To investigate the role of model selection in creating parametric meta-maps for differentiating low- from high-grade STSs.
  • To histopathologically validate the effectiveness of novel parametric meta-maps derived from multiple DWI models.

Main Methods:

  • Quantified DWI data from 28 patients using mono-exponential, bi-exponential, stretched-exponential, and diffusion kurtosis models.
  • Utilized Akaike Weights (AW) for pixel-wise model selection within tumors.
  • Generated pseudo-colorized classification maps and subsequently meta-maps for histological validation.

Main Results:

  • Histological analysis confirmed the accuracy of model suitability in tumor subregions.
  • Three histogram metrics derived from meta-maps demonstrated statistically significant differentiation between low- and high-grade STSs.
  • Achieved an Area Under the Curve (AUC) greater than 89% for differentiating tumor grades.

Conclusions:

  • Integrating model selection into diffusion parametric map design enhances discriminatory power for grading STSs.
  • The developed meta-maps and derived histogram metrics show high potential for clinical application in STS grading.