Deep learning models in histopathology increasingly rely on synthetic data augmentation, yet its impact on model interpretability remains unexplored. This study investigates whether synthetic image generation enhances or degrades concept-based explainability in mitotic cell classification. Using the AMi-Br dataset to develop our concept-based approach, we compare a baseline classifier trained on real images against one augmented with synthetic data. We introduce a post-hoc multi-centroid framework to map latent embeddings to expert-defined biological phenotypes pre-annotated in the AMi-Br dataset, evaluating the semantic structure via novel metrics: Global Separability, Structure Purity, Concept Fracture, and Concept Relevance. Our analysis reveals a critical trade-off: synthetic augmentation acts as a powerful manifold regularizer, significantly improving local neighborhood purity and discriminative power for underrepresented classes. However, this comes at the cost of semantic coherence; specifically, we observe a significant increase in concept fracture that degrades the unity of the histopathological concepts. We conclude that while synthetic data boosts robustness, it fragments biological concepts, necessitating rigorous structural auditing with biological experts to ensure trustworthy clinical decision support.