Radiogenomics, the study of the relationships between imaging features and genomic data, has emerged as a promising approach for understanding complex diseases like Alzheimer’s and Parkinson’s. Coupled with advancements in deep learning (DL) and machine learning (ML), this multidisciplinary field offers new possibilities for disease prediction, early detection, and treatment planning. The integration of multimodal data—combining imaging (radiomics) and transcriptomic or genetic data—holds significant potential for improving the accuracy of disease models.
The paper (https://www.nature.com/articles/s41398-024-02836-9) published in Nature highlights the use of Alzheimer's Disease Neuroimaging Initiative (ADNI) data, which now includes both radiomic and transcriptomic datasets. The hypothesis proven in the study demonstrates the feasibility of using these multimodal datasets to predict and detect neurodegenerative diseases like Alzheimer’s. This opens avenues for a broader exploration of how combining genomic data with radiomics can lead to improved diagnostic models, particularly when paired with powerful ML/DL techniques. The integration of transcriptomics allows a more nuanced understanding of disease pathways and progression, potentially improving predictive power compared to radiomics alone.
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