Ossifying fibroma is a slow-growing benign neoplasm that occurs most often in the jaws, especially the mandible. The tumor is composed of bone that develops within fibrous connective tissue. Some ossifying fibromas consist of cementum-like calcifications, while others contain only bony material; however, a mixture of these calcification types is commonly seen in a single lesion. Of the craniofacial bones, the mandible is the most commonly involved site, with the lesion typically inferior to the premolars and molars. Ossifying fibroma of the jaw shows a female predominance. Some reports of ossifying fibroma have been published in the literature; however, this report continues the research on this topic by detailing 3 types of ossifying fibroma findings on panoramic radiographs and cone-beam computed tomographic images of 4 patients. The radiographs of the presented cases could help clinicians understand the variations in the radiographic appearance of this lesion.

25-hydroxycholesterol (25-HC) is an oxysterol synthesized from cholesterol by cholesterol-25-hydroxylase during cholesterol metabolism. The aim of this study was to verify whether 25-HC induces oxiapoptophagy in fibroblasts. 25-HC not only decreased the survival of L929 cells, but also increased the number of cells with condensed chromatin and altered morphology. Fluorescence-activated cell sorting results showed that there was a dose-dependent increase in the apoptotic populations of L929 cells upon treatment with 25-HC. 25-HC-induced apoptotic cell death was mediated by the death receptor-dependent extrinsic and mitochondria-dependent intrinsic apoptosis pathway, through the cascade activation of caspases including caspase-8, -9, and -3 in L929 cells. There was an increase in the levels of reactive oxygen species and inflammatory mediators such as inducible nitric oxide synthase, cyclooxygenase-2, nitric oxide, and prostaglandin E2 in L929 cells treated with 25-HC. Moreover, 25-HC caused an increase in the expression of beclin-1 and microtubule-associated protein 1A/1B-light chain 3, an autophagy biomarker, in L929 cells. There was a significant decrease in the phosphorylation of protein kinase B (Akt) in L929 cells treated with 25-HC. Taken together, 25-HC induced oxiapoptophagy through the modulation of Akt and p53 cellular signaling pathways in L929 cells.

A 40-year-old man suffered from a repeatedly recurrent desmoplastic ameloblastoma in the right maxillary anterior and premolar regions. During the first visit, the patient was provisionally histopathologically diagnosed with a developmental cyst, and it was confirmed to be unicystic ameloblastoma and resected. Four years later, the lesion recurred, and was diagnosed as a desmoplastic type of ameloblastoma and removed again. Then, 5 years after the second surgery, the lesion recurred again, and was diagnosed as a type containing a follicular pattern, recurrent ameloblastoma. A panoramic radiograph showed a multilocular and mixed radiolucent/radiopaque expansile lesion at the first visit, a unilocular cystic lesion confined to the premolar area at the second visit, and a small soap bubble appearance in the molar area in the final visit. Cone-beam computed tomographic images of the final recurrence of the tumor revealed multiple small cyst-like structures in the right maxillary anterior and posterior regions.

Kim JS. Imaging Sci Dent. 2011 Sep;41(3):135-137. https://doi.org/10.5624/isd.2011.41.3.135

Background: Tongue squamous cell carcinoma (TSCC) is an aggressive oral malignancy characterized by early submucosal invasion and a high risk of cervical lymph node metastasis. Accurate and timely diagnosis is essential, but it remains challenging when relying solely on conventional imaging and histopathology. This systematic review aimed to evaluate studies applying artificial intelligence (AI) in the diagnostic imaging of TSCC. Methods: This review was conducted under PRISMA 2020 guidelines and included studies from January 2020 to December 2024 that utilized AI in TSCC imaging. A total of 13 studies were included, employing AI models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest (RF). Imaging modalities analyzed included MRI, CT, PET, ultrasound, histopathological whole-slide images (WSI), and endoscopic photographs. Results: Diagnostic performance was generally high, with area under the curve (AUC) values ranging from 0.717 to 0.991, sensitivity from 63.3% to 100%, and specificity from 70.0% to 96.7%. Several models demonstrated superior performance compared to expert clinicians, particularly in delineating tumor margins and estimating the depth of invasion (DOI). However, only one study conducted external validation, and most exhibited moderate risk of bias in patient selection or index test interpretation. Conclusions: AI-based diagnostic tools hold strong potential for enhancing TSCC detection, but future research must address external validation, standardization, and clinical integration to ensure their reliable and widespread adoption.