Early diagnosis of cancer in its initial stages, when the tumor is confined in a small area, increases the probability of survival. Screening at-risk populations, suspect for cancer development, is suggested by the doctors as a vital tool to diagnose the disease at an early stage, when the treatment has more chances to succeed. The most popular screening tests include Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and PET/CT scans. The area of ‘radiomics’ has focused on the extraction of quantitative features from medical images in order to reveal the development and progression of cancer, providing valuable information for clinical diagnosis and treatment planning. In radiomics studies imaging characteristics are accumulated in a massive set of features, which entail either qualitative and quantitative image descriptors measured within the segmented volume of interest or features “engineered” through advanced AI techniques and deep learning. Such features might be considered as byproducts and manifestations of the genomic variation at the cellular level, which controls the specific disease phenotype and/or response to treatment. Alternatively, the genotypic markers from molecular biology reflect many aspects of gene and protein interactions across a variety of cellular processes relevant to cancer diagnosis and prognosis. Considering the imaging/radiomic features as “surrogates” of the genetic substrate, this talk addresses the intersection of the traditional analysis of differential genes and the feature extraction from medical images. More specifically, we consider the interaction among imaging features (e.g. size and/or shape features, image intensity histogram metrics, texture features) and RNA transcripts measurements (expression of selected genes), which can be used to enhance the predictive power in the diagnosis of cancer. Following this consideration, we explore the modeling of radio-transcriptomics correlations based on the modeling of associations between these two multiscale modalities, i.e. imaging and genomics.