Paulo Victor Mendes Penafort

Objectives: This study aimed to investigate the clinicopathological features of malignant odontogenic tumors (MOTs) and to provide a comprehensive characterization that enhances the current understanding of this disease group. Methods: MOTs were retrieved from six Brazilian oral pathology services between 2007 and 2025. Demographic, clinical and radiographic data were collected from the charts, and the histological H&E-stained sections were revised by two blinded pathologists; previously available immunohistochemical and molecular findings were retrieved and reviewed in selected cases and the diagnosis was established according to the 2022 World Health Organization classification. Results: A total of 53 malignant odontogenic tumors were identified, predominantly affecting the mandible (83%), with a slight male predominance (male-to-female ratio, 1.12:1) and a mean age of 41.4 years. Ameloblastic carcinoma was the most frequent subtype (58.5%), followed by clear cell odontogenic carcinoma (18.9%), ameloblastic fibrosarcoma (13.2%), primary intraosseous carcinoma (7.5%), and ghost cell odontogenic carcinoma (1.9%). Conclusions: This multicenter review summarizes the main features of malignant odontogenic tumors. Although rare, these tumors often exhibit locally aggressive behavior, highlighting the need for early diagnosis, accurate histopathological evaluation, and standardized reporting with long-term follow-up.

Recurrent aphthous stomatitis is a common inflammatory condition of the oral mucosa characterized by painful ulcers that heal spontaneously but recur unpre

Background: Rhabdomyosarcoma (RMS), a rare malignant tumor, frequently affects pediatric patients, with 35%–40% occurring in the head and neck. This study analyzes the clinicopathologic profile of pediatric head and neck rhabdomyosarcomas from Brazil, Guatemala, Mexico, and South Africa.Methods: We reviewed 44 cases from 10 Oral and Maxillofacial Pathology services, conducting immunohistochemical analyses of desmin, myogenin, Myo‐D1, and Ki67, with quantification via QuPath software. Cases with ≥ 50% myogenin expression were tested for fusion status using AP2β, NOS‐1, and HMGA2. Statistical analyses included the Kruskal–Wallis test for age and marker expression comparisons, Fisher's exact test for categorical variables, Spearman's rank correlation for marker relationships, and multinomial logistic regression to assess fusion status likelihood.Results: Cases were predominantly from Brazil (40.9%), followed by South Africa (27.3%), Guatemala (22.7%), and Mexico (9.1%). Two‐thirds of patients were diagnosed in their first decade with no gender predilection. Nonparameningeal sites (45.5%) were more affected than parameningeal (40.9%) and orbital sites. Microscopically, embryonal RMS (77.3%) was most common, followed by alveolar (18.2%) and spindle cell (2.3%) tumors. Immunohistochemistry revealed positivity for myogenic markers, with significant differences in myogenin expression between embryonal and alveolar RMS variants (p < 0.05). Fusion status prediction identified two potential fusion‐positive alveolar RMS cases, while all embryonal RMS and one alveolar RMS case appeared fusion‐negative. Significant correlation with positive fusion status was found only between AP2β and NOS1 (p < 0.05).Conclusion: Although there are slight clinical‐demographic variations among pediatric head and neck rhabdomyosarcomas in these regions, identifying fusion status through immunohistochemistry remains a diagnostic challenge.

Objectives: To document the case of a patient who underwent several endodontic treatments due to a glandular odontogenic cyst misdiagnosed as an inflammatory periapical lesion.Background: Glandular odontogenic cysts behave more aggressively, while others have an indolent course. There is limited information on this cyst in the gerodontologic literature.Materials and Methods: A 76‐year‐old male patient presented with an asymptomatic expansive lesion in the anterior mandible resistant to several endodontic treatments. Cone‐beam computed tomography revealed a multilocular osteolytic lesion measuring 6.0 × 4.0 cm, with cortical bone perforation.Results: Histopathological analysis of a biopsy specimen was consistent with glandular odontogenic cyst. The patient underwent marginal mandibulectomy with preservation of the base of the mandible.Conclusion: A strict diagnostic process is important to avoid unwanted consequences, particularly in the geriatric population.

Background: Ameloblastoma and ameloblastic carcinoma are epithelial odontogenic tumors that can be morphologically similar. In the present study, we evaluated the DNA content and Ki‐67 index in the two tumors.Methods: The paraffin blocks of the tumors were selected to obtain sections for the immunohistochemical reactions and preparation of the cell suspension for acquisition in a flow cytometer. The Random Forest package of the R software was used to verify the contribution of each variable to classify lesions into ameloblastoma or ameloblastic carcinoma.Results: Thirty‐two ameloblastoma and five ameloblastic carcinoma were included in the study. In our sample, we did not find statistically significant differences in Ki‐67 labeling rates. A higher fraction of cells in 2c (G1) was correlated with the diagnosis of ameloblastoma, whereas higher rates of 5c‐exceeding rate (5cER) were correlated with ameloblastic carcinoma. The Random Forest model highlighted histopathological findings and parameters of DNA ploidy study as important features for distinguishing ameloblastoma from ameloblastic carcinoma.Conclusion: Our findings suggest that the parameters of the DNA ploidy study can be ancillary tools in the classification of ameloblastoma and ameloblastic carcinoma.

Background: Odontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis. The main advantages of integrating Machine Learning (ML) with microscopic and radiographic imaging is the ability to significantly reduce intra‐and inter observer variability and improve diagnostic objectivity and reproducibility.Methods: Thirty Digitized slides were collected from different diagnostic centers of oral pathology in Brazil. After performing manual annotation in the region of interest, the images were segmented and fragmented into small patches. In the supervised learning methodology for image classification, three models (ResNet50, DenseNet, and VGG16) were focus of investigation to provide the probability of an image being classified as class0 (i.e., ameloblastoma) or class1 (i.e., Ameloblastic carcinoma).Results: The training and validation metrics did not show convergence, characterizing overfitting. However, the test results were satisfactory, with an average for ResNet50 of 0.75, 0.71, 0.84, 0.65, and 0.77 for accuracy, precision, sensitivity, specificity, and F1‐score, respectively.Conclusions: The models demonstrated a strong potential of learning, but lack of generalization ability. The models learn fast, reaching a training accuracy of 98%. The evaluation process showed instability in validation; however, acceptable performance in the testing process, which may be due to the small data set. This first investigation opens an opportunity for expanding collaboration to incorporate more complementary data; as well as, developing and evaluating new alternative models.