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

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Abstract

Alzheimer's disease (AD) is a heterogeneous neurodegenerative disorder driven by complex interactions between genetic susceptibility, molecular pathways, and progressive brain alterations. Key genetic factors, including <i>APOE</i>, <i>TREM2</i>, and <i>MAPT</i>, contribute to pathological processes such as amyloid-β accumulation, tau aggregation, neuroinflammation, and synaptic dysfunction. Despite substantial advances in understanding these mechanisms, translating molecular insights into clinically accessible biomarkers remains a major challenge. Radiomics and machine learning (ML) have emerged as promising approaches for extracting high-dimensional quantitative features from medical imaging data and identifying complex patterns associated with disease processes. Radiomic features capture spatial heterogeneity and subtle characteristics of neurodegeneration that are not discernible using conventional imaging analysis. When integrated with ML, these features may serve as noninvasive surrogates of molecular activity, enabling the identification of imaging signatures associated with specific genetic backgrounds and biological pathways. This review aims to explore how radiomics and ML can bridge the gap between genetic and molecular mechanisms and in vivo imaging phenotypes in AD. We summarize current knowledge on genetic determinants and molecular pathways and discuss advances in molecular imaging, particularly tracers targeting amyloid and tau pathology. Furthermore, we analyze the emerging role of radiomics and ML in linking imaging phenotypes with underlying biological processes. This integrative framework may support improved disease stratification, early diagnosis, and prediction of therapeutic response, contributing to the development of precision medicine strategies and future theranostic approaches in Alzheimer's disease.

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