RADIOGENOMICS IN NEUROLOGICAL DISORDERS: A REVIEW OF IMAGING-GENETIC INTERACTIONS AND IMPLICATIONS FOR PRECISION MEDICINE

Abstract

Radiogenomics has emerged as an innovative interdisciplinary approach that integrates imaging features with genomic and molecular data to better understand the biological basis of neurological disorders. This approach addresses limitations of conventional diagnostic methods by capturing disease heterogeneity and enabling more precise characterization of complex neurological conditions. This review aims to evaluate the role of radiogenomics in neurological disorders, focusing on imaging-genetic interactions, technological advancements, and implications for precision medicine. A narrative analysis of recent literature was conducted, emphasizing studies on radiomics, imaging genetics, artificial intelligence, and multimodal data integration. Key areas explored include neuro-oncology, neurodegenerative disorders, and psychiatric conditions, along with emerging computational and translational approaches. Radiogenomics demonstrates significant potential in improving early diagnosis, risk prediction, and disease characterization. Integration of artificial intelligence and machine learning enhances predictive modeling, tumor grading, and treatment response evaluation. Multimodal data fusion and systems biology approaches provide deeper insights into disease mechanisms and support biomarker discovery. Applications across glioma, Alzheimer’s disease, Parkinson’s disease, and psychiatric disorders highlight its versatility and clinical relevance. Radiogenomics represents a powerful tool for advancing precision medicine in neurology. Its ability to combine imaging and genomic data enables personalized treatment strategies, improved prognostic assessment, and non-invasive biomarker development. Continued advancements in computational methods and clinical integration are expected to further enhance its impact on neurological healthcare.