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Explainable machine learning radiomics model for Primary Progressive Aphasia classification.

Benedetta Tafuri1,2, Roberto De Blasi2, Salvatore Nigro2

  • 1Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy.

Frontiers in Systems Neuroscience
|April 2, 2024
PubMed
Summary

This study shows radiomics analysis of brain scans accurately distinguishes subtypes of Primary Progressive Aphasia (PPA), a language disorder. Machine learning identified key white matter features for diagnosing semantic (svPPA) and non-fluent (nfvPPA) variants.

Keywords:
MRIPrimary Progressive Aphasiaexplainabilitymachine learning (ML)radiomics

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Neurology

Background:

  • Primary Progressive Aphasia (PPA) is a neurodegenerative disease causing language impairment.
  • Key subtypes include semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants.
  • Accurate diagnosis requires integrating clinical, biological, and radiological data.

Purpose of the Study:

  • To assess the utility of white matter texture analysis using radiomics for PPA classification.
  • To employ explainable machine learning to identify critical features for differential diagnosis.
  • To improve diagnostic accuracy for svPPA and nfvPPA.

Main Methods:

  • White matter texture analysis on T1-weighted MRI scans from 56 PPA patients and 53 controls.
  • Training a tree-based algorithm with combined clinical and radiomics measures.
  • Utilizing Shapley Additive Explanations (SHAP) for feature importance analysis.

Main Results:

  • Radiomics models achieved 95% accuracy distinguishing svPPA from controls and 93.7% distinguishing svPPA from nfvPPA.
  • nfvPPA patients were differentiated from controls with 93.7% accuracy.
  • SHAP analysis highlighted white matter near the left entorhinal cortex as crucial for classification.

Conclusions:

  • Radiomics features are valuable for classifying svPPA and nfvPPA subtypes.
  • Explainable AI effectively identifies key diagnostic features in PPA.
  • This approach enhances the differential diagnosis of PPA.