Background: Breast microcalcifications (MCs) are key early indicators of breast cancer. In mammography, radiomics features are commonly computed once on 2D MC clusters and at a single segmentation scale. This prevents testing how the discriminative signal for benign/malignant MC classification changes from the densely calcified core to progressively larger surrounding regions, or whether it is best captured by how features evolve as the analyzed MCs are systematically expanded. Using high-resolution 3D ex-vivo micro-CT, we quantify benign/malignant discrimination along a per-MC erosion–dilation trajectory. Materials and Methods: Biopsies from 94 patients were scanned with ex-vivo micro-CT and 3504 MCs were segmented. For each MC, we generated 11 segmentation masks by applying erosions Ek and dilations Dk (k ∈ 2,4,6,8,10) around the baseline mask M0; Ek/Dk denote erosion/dilation stage k. Analyses were restricted to MCs with complete trajectories (i.e., not vanishing under erosion), yielding 84 patients and 608 MCs. Radiomic features were extracted at each stage and evaluated with patient-level classification comparing: (i) separate models for each stage of the erosion-dilation trajectory t, (ii) a single and same 80-feature subset reused across all stages, and (iii) a trajectory model concatenating these 80 features across stages. Results: Dense cores (E10) retained diagnostic signal (AUC of 0.71) compared to M0, while performance increased towards intermediate dilations, peaking at D8 (AUC of 0.851). Reusing the same 80 features improved AUC at every stage, with the largest gain at E10 (AUC improvement 0.4). Using the 11 segmentations yields a trajectory model which further increased AUC to 0.865. Conclusions: Radiomics of individual MCs reveals extra information when computed on an erosion–dilation trajectory, rather than on a single segmentation mask.