Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC.

Odontomas are benign odontogenic tumors formed from epithelial and mesenchymal cells. They are mostly associated with disorders of tooth eruption, causing impaction and/or delayed tooth eruption, and are an accidental finding on routine radiological examination. The aim of this paper is to present current findings in the etiology and treatment of odontomas, as well as the clinical and radiographic features, describing a case that is rarely found in the literature. A case of multiple complex odontoma in the mandible of an 11-year-old boy is presented, causing impaction of the first permanent right molar, 46. The treatment consisted of surgical enucleation of the multiple complex odontoma with preservation of the impacted tooth, monitoring clinically and radiologically its spontaneous eruption followed by final orthodontic alignment. Odontomas are not an everyday part of clinical practice and given that they are most commonly associated with permanent tooth impaction, it is extremely important to have knowledge of their clinical and radiological features. Early diagnostics and appropriate treatment result in better diagnosis, thus increasing the possibility of preserving the impacted teeth.

This study aimed to assess the relationship and possible interactions between metallothioneins (MTs) and megalin (LRP-2) in different grades of oral squamous cell carcinoma (OSCC) and premalignant lesions of the oral mucosa (oral leukoplakia and oral lichen planus). The study included archived samples of 114 patients and control subjects. Protein expression was examined by immunohistochemistry and immunofluorescence, and staining quantification was performed by ImageJ software. Protein interaction in cancer tissue was tested and visualized by proximity ligation assay. Mann-Whitney and Kruskal-Wallis tests were used to determine the significance of differences between each group, whereas Pearson correlation coefficient was performed to test correlation. Expression of both proteins differed significantly between each group showing the same pattern of gradual increasing from oral lichen planus to poorly differentiated OSCC. Moreover, MTs and megalin were found to co-express and interact in cancer tissue, and their expression positively correlated within the overall study group. Findings of prominent nuclear and chromosomal megalin expression suggest that it undergoes regulated intramembrane proteolysis upon MTs binding, indicating its ability to directly affect gene expression and cellular division in cancer tissue. The data obtained point to the onco-driving potential of MTs-megalin interaction.

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Chemotherapy used on pediatric patients especially those below 3 years of age causes disturbances in dental development. The aim of this case report was to present the late dental effects of chemotherapy in a patient treated for anaplastic ependymoma (WHO III) at an early age. Radiographic findings at the age of 9 years showed oligomicrodontia of six teeth, maxillary lateral incisors, and maxillary and mandibular first premolars. Pediatric cancer survivors after chemotherapy have an increased risk of one or more dental development disorders. To ensure proper dental care and to assess the long-term effects on oral health, tooth development, and occlusion, the involvement of a dentist is crucial. Adequate diagnosis and well-planned treatment of the dental defect can significantly improve patient oral health-related quality of life.

Megalin (LRP2) is a rapidly recycling multiligand endocytic receptor primarily expressed in polarized epithelial cells. Although megalin might be involved in tumor growth and invasiveness through several mechanisms, its role has been understudied in the field of molecular oncology so far. The present study aimed to evaluate the impact of megalin expression in oral squamous cell carcinoma (OSCC) on disease progression. Megalin expression was evaluated immunohistochemically in 63 OSCC specimens. Data obtained were retrospectively compared with patient clinicopathological features and their survival. The proportion of megalin-expressing cells in the primary OSCC tissue was significantly associated with metastatic spreading to lymph nodes, vascular invasion and lower overall survival rate. Results obtained by the study suggest that megalin can be considered as a novel molecule involved in OSCC pathogenesis, but also useful as a potential biomarker for cancer progression.

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Oral cancer (OC) is among the top ten cancers worlwide, with more than 90% being squamous cell carcinoma. Despite diagnostic and therapeutic development in OC patients’ mortality and morbidity rates remain high with no advancement in the last 50 years. Development of diagnostic tools in identifying pre-cancer lesions and detecting early-stage OC might contribute to minimal invasive treatment/surgery therapy, improving prognosis and survival rates, and maintaining a high quality of life of patients. For this reason, Artificial Intelligence (AI) algorithms are widely used as a computational aid in tumor classification and segmentation to help clinicians in the earlier discovery of cancer and better monitoring of oral lesions. In this paper, we propose an AI-based system for automatic segmentation of the epithelial and stromal tissue from oral histopathological images in order to assist clinicians in discovering new informative features. In terms of semantic segmentation, the proposed AI system based on preprocessing methods and deep convolutional neural networks produced satisfactory results, with 0.878 ± 0.027 mIOU and 0.955 ± 0.014 F1 score. The obtained results show that the proposed AI-based system has a great potential in diagnosing oral squamous cell carcinoma, therefore, this paper is the first step towards analysing the tumor microenvironment, specifically segmentation of the microenvironment cells.