Virtual clinical trials (VCTs) have been proposed to overcome the limitations of clinical trials using a patient population. VCTs are in-silico reproductions of medical examinations using digital models of the patients and simulated imaging devices. In this work, we present a VCT framework for imaging and dosimetry in breast computed tomography (BCT), digital breast tomosynthesis (DBT) and 2D digital mammography (DM), realized by Univ. Napoli Federico II in collaboration with the medical physics teams at Univ. California Davis and the Medical University of Varna, Bulgaria. Computational phantoms of the uncompressed (pendant) breast were generated by clinical BCT scans acquired at UC Davis. A dataset of digital breast phantoms was produced by means of voxel classification of the uncompressed breast CT images. The voxels were classified as air, skin, adipose and glandular tissue using a semi-automatic algorithm. A software compression algorithm (developed at U. Varna) applied to the 3D phantoms produces compressed breast digital phantoms for virtual DM and DBT investigations using a clinical scanners' technical specifications and geometry as inputs. Monte Carlo simulations, based on Geant4, were used to provide in-silico reproductions of real scans of a given patient breast model. The software permits the estimation of mean glandular dose (MGD) in 2D and 3D imaging as well as the 3D dose distribution. The platform produces breast projection images which are then reconstructed using analytical or iterative algorithms. Patient-specific MGD estimations, as well as simulated BCT volume data sets were compared with the clinical BCT scans. The VCT platform reported herein will be used for scanner optimization and for virtual trials comparing BCT against mammography and DBT, in terms of image quality and glandular dose distributions. In addition to in-silico evaluation, 3D printing methods were used to produce compressed and uncompressed anthropomorphic breast phantoms from the patient image-derived digital breast phantoms for the purpose of experimental validation.