MIXED odontogenic tumors can be quantitatively distinguished using digital morphometric analysis, with virtual cell-based methods revealing subtle histological differences that may aid diagnosis in challenging cases.
Quantitative Approach to Mixed Odontogenic Tumors
Accurate diagnosis of mixed odontogenic tumors can be difficult because of overlapping histological features between tumour entities and developing dental structures. This study aimed to characterise the histomorphology of several mixed odontogenic tumors using mathematical morphology algorithms applied to digitised histological images. A total of 20 cases were analysed, including primordial odontogenic tumor, ameloblastic fibroma, developing odontoma, and tooth germs. Histological sections stained with haematoxylin and eosin were digitised, and epithelial compartments were segmented into virtual cells to enable detailed quantitative assessment of tissue architecture.
Differences In Virtual Epithelial Cell Morphology
The analysis compared the mean area of virtual epithelial cells across the four entities. Although overall data distribution patterns were similar, statistically significant differences were identified between groups: p<0.001. Developing odontoma demonstrated the largest mean virtual epithelial cell area, while ameloblastic fibroma showed the smallest. In addition, developing odontoma exhibited a broader distribution of epithelial cell areas, suggesting greater architectural heterogeneity compared with the other entities. These findings indicate that digital morphometric parameters can capture differences that may not be readily apparent on routine qualitative histological assessment.
Implications For Diagnostic Practice
No statistically significant differences were observed between tooth germs and primordial odontogenic tumor lacking subepithelial condensation. Notably, quantitative tissue analysis revealed that, in focal areas, primordial odontogenic tumor more closely resembled tooth germs than other mixed odontogenic tumors. This observation supports the concept that primordial odontogenic tumor may share developmental features with normal odontogenesis. The authors suggest that virtual cell based morphometric analysis could serve as a complementary tool in diagnostically challenging cases, providing objective quantitative data alongside conventional histopathological evaluation. However, they emphasise that validation in larger datasets is required before broader clinical application. Overall, the study highlights the potential of digital pathology approaches to refine classification and improve understanding of mixed odontogenic tumors.
Reference
Pereira-Prado V et al. Algorithmic analysis of the structure of mixed odontogenic tumors. Scientific Reports. 2026; https://doi.org/10.1038/s41598-026-38399-6.
