From Genes to Imaging Phenotypes: Radiomics and Machine Learning as Tools to Decode Molecular Pathways in Alzheimer's...

Area of Science:

• Neuroimaging
• Genetics
• Computational Biology

Background:

• Alzheimer's disease (AD) pathogenesis involves complex genetic, molecular, and neurodegenerative processes.
• Translating molecular insights into accessible biomarkers for AD remains a significant challenge.
• Existing imaging analysis often misses subtle neurodegenerative characteristics.

Purpose of the Study:

• To explore the integration of radiomics and machine learning (ML) in Alzheimer's disease research.
• To bridge the gap between genetic/molecular mechanisms and in vivo imaging phenotypes in AD.
• To discuss the potential of radiomics and ML for improved AD diagnosis and treatment.

Main Methods:

• Review of current knowledge on AD genetic determinants and molecular pathways.
• Analysis of advances in molecular imaging, including amyloid and tau tracers.
• Examination of radiomics and ML techniques for extracting quantitative imaging features.
• Integration of imaging phenotypes with underlying biological processes.

Main Results:

• Radiomics extracts high-dimensional features from medical images, capturing spatial heterogeneity.
• ML algorithms identify complex patterns in radiomic data, correlating with disease processes.
• Radiomic features can serve as noninvasive surrogates for molecular activity and genetic backgrounds.
• Emerging evidence links imaging phenotypes to specific biological pathways in AD.

Conclusions:

• Radiomics and ML offer a promising framework for understanding AD.
• This integrative approach can enhance disease stratification, early diagnosis, and treatment response prediction.
• The findings support the development of precision medicine and theranostic strategies for Alzheimer's disease.