Changes in 3D radiomic texture descriptors in Alzheimer’s disease stages

Abstract

Alzheimer's disease (AD) is a neurodegenerative disease that induces cognitive and functional decline in the elderly. Detecting AD in its early, pre-dementia stages (i.e., during mild cognitive impairment, MCI) is challenging as the rate of atrophy varies among brain regions. This progressive degeneration, nevertheless, potentially can be identified in brain imaging by examining changes in texture descriptors. Textures in images are thought to reveal underlying patterns of structural change in brain tissue. In addition, careful examination of a panel of texture descriptors may allow for improved prediction of disease stages. This study evaluates four common groups of texture descriptors (GLCM-gray-level co-occurrence matrix, GLRLM-gray-level run-length matrix, GLSZMgray-level size zone matrix, and NGTDM-neighbouring gray tone difference matrix) across 85 anatomical brain regions in three clinical populations (HN -healthy normal, MCI, and AD). Specifically, using statistical hypothesis validation at the regional level, differences in texture descriptors were analyzed in three comparative scenarios (HN vs MCI, HN vs AD, and MCI vs AD) and multiple comparisons were corrected using false discovery rate (FDR). From sixty-one examined texture descriptors, only six (9.8%) were found to be statistically significant (p < 0.05) in all three comparative scenarios after FDR correction -five were GLRLM features and one was GLCM. Our results highlighted significant changes in texture descriptors among HN, MCI, and AD populations, and they align with existing biological and computational findings in the literature.