A framework for simulation of temporal evolution and longitudinal studies of breast anatomy in radiology

Abstract Background Virtual imaging trials (VITs) are in silico studies that simulate medical imaging and disease processes, offering a cost‐effective and reproducible addition to traditional imaging trials. While VITs are well established in breast imaging, most existing implementations simulate imaging based on static anatomical models, capturing only a single time point. This limits their ability to study time‐dependent processes such as tumor progression or breast density and composition changes over time. Purpose We introduce STELLA‐R ( S imulation of T emporal E volution and L ongitudina L studies of breast A natomy in R adiology) , the first framework aimed at performing longitudinal virtual imaging trials in breast imaging. STELLA‐R is designed to simulate temporal changes in breast anatomy, density, and lesion development across a virtual population of women. Methods Our simulation pipeline consists of five modules. The population creator module generates realistic virtual cohorts based on real‐world data from approximately 25 000 women, modeling multivariate distributions of age, breast shape, and breast density. The phantom creator and lesion creator modules enable detailed specification of breast and lesion characteristics, utilizing Perlin noise computational algorithms to replicate tissue appearance. The tumor location is assigned in the lesion insertion module. To simulate temporal changes in the breast, we used real‐world data from two consecutive screening rounds. This enabled realistic modeling of mammographic density evolution, breast volume changes, and tumor growth. Different breast densities were achieved by adjusting threshold values applied to the Perlin noise, which determines the amount of tissue structure. Temporal changes of parenchyma were simulated by gradually varying the threshold values. Tumor progression was simulated by increasing lesion size according to growth rates sampled from real‐world data. Lastly, the Image Generation module integrates multiple external software components for mammographic image formation, including noise and scatter simulation and image reconstruction. In this study, we simulated digital breast tomosynthesis (DBT) images of our phantoms using open‐source tools. Our simulation framework is modular and can be extended to support additional imaging modalities. Results We demonstrate case examples of virtual women at ages 40, 57, and 74, reflecting Swedish screening intervals, and report simulated changes in volumetric breast density over time (14%, 9%, and 6%, respectively). The breast density is modeled with a mean accuracy of < 2% compared to target values. Additionally, we illustrate lesion progression across multiple time points, assuming a tumor doubling time of 282 days. Our fitted models accurately capture correlations between age, breast volume, density, and annual changes. Conclusion STELLA‐R pipeline provides a novel foundation for evaluating long‐term screening strategies, imaging, and risk models in a controlled and customizable manner using longitudinal VITs.