PURPOSE: Radiomics has revolutionized clinical research by enabling objective measurements of imaging-derived biomarkers. However, the true potential of radiomics necessitates a comprehensive understanding of the biological basis of extracted features to serve as a clinical decision support. In this work, we propose an end-to-end framework for the in silico simulation of [ METHODS: We considered 4 immunohistochemically stained Whole Slide Images of pancreatic tissue of one healthy control and three patients with PDAC and/or precursor lesions. From marker-specific images, tissue-depending diffusivity properties were estimated and computational domains were built to simulate the [ RESULTS: The framework captured the phenotypic differences and generated Time Activity Curves reflecting the underlying tissue composition. Image-derived biomarkers were ranked in view of their association with biological characteristics of the tissue, unveiling their molecular correlative. Moreover, we showed that the proposed pipeline could serve as a digital phantom to optimize the image acquisition for lesion detection. CONCLUSIONS: This innovative framework holds the potential to enhance interpretability and reliability of radiomics, fostering the adoption in personalized nuclear medicine and patient care.