Understanding the pathogenesis of odontogenic cysts (OCs) may facilitate the development of alternative therapeutic strategies and improve patients' quality of life. Given calcitonin's role in regulating bone metabolism and its clinical applications in gnathic bone-destructive lesions, including OCs, this study assessed potential associations between CTR and the bone resorption markers receptor activator of nuclear factor kappa B ligand (RANKL) and tumor necrosis factor alpha (TNF-α) in OCs. Immunohistochemical analyses were performed on 20 radicular cysts (RCs), 20 radicular residual cysts (RRCs), and 27 dentigerous cysts (DCs). RANKL expression in the epithelial lining was significantly higher in RCs and RRCs compared with DCs ( p =0.039 and p =0.046, respectively). In RCs, significant positive correlations were found between epithelial RANKL and capsular CTR ( p =0.039), epithelial TNF-α and capsular TNF-α ( p =0.037), and epithelial TNF-α and capsular CTR ( p =0.005). In RRCs, epithelial RANKL correlated positively with epithelial TNF-α ( p =0.007) and capsular CTR correlated positively with capsular TNF-α ( p =0.041), whereas epithelial RANKL correlated negatively with capsular TNF-α ( p =0.009). In DCs, significant positive correlations were observed between epithelial and capsular RANKL ( p <0.001) and between epithelial and capsular TNF-α ( p =0.019). These findings suggest that CTR contributes to the pathogenesis of DCs, RCs, and RRCs, and highlight the involvement of RANKL and TNF-α in the biological behavior of these cysts. The interplay among these proteins may promote either osteolytic or osteogenic activity depending on the cystic microenvironment.

Background/Objectives: Automated localization of cystic lesions and benign tumors on panoramic radiographs may support lesion recognition in the maxillofacial region. This preliminary feasibility study aimed to develop and evaluate a deep learning model based on Mask R-CNN for the localization of dentigerous cysts (DCs), radicular cysts (RCs), odontogenic keratocysts (OKCs), and ameloblastomas using panoramic radiographs. Methods: A total of 215 panoramic radiographs were retrospectively collected from Taichung Veterans General Hospital (2018-2023). After excluding postoperative, recurrent, or low-quality images, 184 lesions were allocated to the training set and 47 lesions to the testing set. Lesions were annotated based on pathology-confirmed diagnoses. The Mask R-CNN model was trained to localize and classify four lesion types. Model performance was evaluated using precision, sensitivity (recall), and F1 score at an Intersection over Union (IoU) threshold of 0.1. Results: In the testing set (n = 47), 26 lesions were correctly localized, yielding an overall sensitivity of 55.3% and a precision of 83.9%. The corresponding F1 score was 66.7%. Lesion-specific sensitivities were 40.0% for ameloblastomas, 37.5% for OKCs, 36.8% for RCs, and 93.3% for DCs. Conclusions: This study suggests the preliminary feasibility of a deep learning-assisted approach for lesion localization on panoramic radiographs. However, the absence of lesion-free control images and the limited dataset size restrict the generalizability and clinical applicability of the findings. Further validation using larger and more balanced datasets is required.

Tumours originating from the odontogenic apparatus exhibit a high level of diversity and complexity. They exhibit a wide range of morphologies because this tissue is formed by time-dependent, closely regulated interactions between mesenchymal and epithelial elements. Notably, hybrid lesions—which consist of two or more separate regions displaying distinctive morphologic traits of different entities—are uncommon and, when they do occur, provide a diagnostic difficulty. Despite the identification of numerous hybrid lesions, their prevalence and combinations remain unknown. Ameloblastoma with Calcifying Epithelial Odontogenic Tumour (CEOT) is an extremely rare hybrid tumour, with five cases reported previously. The current paper reviews the pertinent literature and describes one more instance of a hybrid odontogenic tumour composed of ameloblastoma with CEOT. In the present case, the lesion in the mandible was provisionally diagnosed as a dentigerous cyst based on clinical and radiographic findings. But based on the histopathologic evaluation of the incisional biopsy specimen, the diagnosis of unicystic ameloblastoma with intramural proliferation was given. However, histopathology of the excised tumour revealed areas of plexiform ameloblastoma with mural proliferations along with areas of CEOT. Hence, a final diagnosis of hybrid odontogenic tumour of unicystic ameloblastoma with CEOT was confirmed. Hybrid odontogenic lesions create a diagnostic challenge. Diverse differentiation and intricate inductive connections are possible in odontogenic tumours that arise from odontogenic epithelium. Thus, it may be said that pluripotent odontogenic epithelium simultaneously causes the development of such disparate histopathological patterns inside a single tumour, resulting in the development of a hybrid tumour.

Accurate diagnosis of odontogenic lesions requires pre-operative cone-beam computed tomography (CBCT) and post-operative histopathological confirmation, a workflow that is time-consuming and reliant on clinical expertise. With the rise of artificial intelligence (AI) and deep learning, automated diagnostic solutions have shown great promise. However, progress in deep learning for odontogenic lesions has been hindered by the lack of publicly available paired datasets that combine radiological and histopathological data. To address this gap, we present the Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset (DOLCHID), comprising 262 paired CBCT scans and H&E-stained histopathology images. The dataset includes four major lesion subtypes - dentigerous cyst (n = 44), radicular cyst (n = 54), odontogenic keratocyst (n = 92), and ameloblastoma (n = 72), each paired with expert-verified CBCT segmentation masks and annotated histopathological regions of interest (ROI). We also provide technical validations for lesion segmentation, single modality classification, and multimodal classification, which demonstrate the utility of our dataset. DOLCHID is expected to advance deep learning research in dental imaging by enabling integrative diagnostic modelling that leverages complementary radiological and histopathological information.

Jaw cysts are a common, yet diverse group of lesions often detected incidentally during routine dental examinations. While many cysts remain asymptomatic, larger, or aggressive, cysts can lead to significant complications, including tooth displacement, jaw fractures and nerve damage. This article reviews the pathophysiology, classification and management of jaw cysts, focusing on odontogenic cysts, which account for most cases presenting to oral and maxillofacial surgery. Various surgical techniques exist, including enucleation, marsupialisation, decompression, and en bloc resection, each with advantages, limitations, and indications based on cyst size, location, and recurrence risk. We present a case study illustrating a successful outcome using a two-staged treatment approach for a large dentigerous cyst. Selecting the appropriate management strategy requires careful consideration of lesion characteristics, patient factors, and recurrence risk, with the goal of minimising morbidity and optimising patient recovery and long-term outcomes.

To determine the prevalence and distribution of dentigerous cysts among unerupted teeth in adult dogs based on age, sex, reproduction status and cranial conformation. Cross-sectional radiographic study. Medical records were reviewed to obtain clinical data, including breed, age, sex and reproduction status. Diagnostic dental radiographic imaging was reviewed. Binomial logistic regression was used to investigate factors contributing to the likelihood of having a cyst in dogs with one or more unerupted teeth. Two hundred and eighty-five unerupted teeth and 95 (33.3%) dentigerous cysts were identified in 206 dogs out of approximately 13,000 records examined between 01/2017 and 06/2023. The highest frequency of unerupted teeth was found on the mandibular first premolars, mandibular third molars and mandibular central incisors. Regardless of tooth type, prevalence per site was under 50% for all but the mandibular second incisor. Brachycephalic dogs had higher odds of having cysts than non-brachycephalic or mixed breed dogs (odds ratios of 3.39 and 2.79), and neutered male dogs had higher odds than intact females (odds ratio of 1.58). Results suggest that close monitoring of unerupted teeth without radiographic evidence of associated cysts may be an appropriate minimally invasive treatment method in lieu of prophylactic extraction.

Background/Objectives: Odontogenic cysts and ameloblastomas (AB) are mostly asymptomatic, often discovered later due to severe symptoms, and only histopathological examination provides definitive diagnosis. AI-assisted diagnostics offer a fast, noninvasive, painless diagnostic tool. To our knowledge, this is the first meta-analysis aiming to evaluate the classification, detection, and segmentation performance of artificial intelligence (AI) for odontogenic cysts and ABs as distinct entities and to determine if it can achieve clinically acceptable accuracy. Methods: Our systematic search was conducted on 11 January 2026, in Medline, EMBASE, and Cochrane Central Register of Controlled Trials without restrictions or filters. Studies comparing AI diagnostics with histopathological diagnostics for odontogenic cysts and ABs were included. Diagnostic parameters, including sensitivity, specificity, and accuracy, were extracted and analyzed; additionally, diagnostic odds ratios were calculated. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Recommendations of the GRADE workgroup were followed to determine the certainty of evidence. Results: Thirteen articles were found eligible, of which seven were included in our meta-analysis. The group with the highest sensitivity (Se) was the "no lesion" (N) group (0.9726, 95% CI 0.9284-1; I2 = 46%), followed by the radicular cyst (RC) (mean 0.9054, 95% CI 0.8051-1; I2 = 89%), dentigerous cyst (DC) (mean 0.8788, 95% CI 0.7828-0.9749; I2 = 93%), odontogenic keratocyst (OKC) (0.763, 95% CI 0.6999-0.8262; I2 = 14%) and AB (mean 0.4369, 95% CI 0.231-0.6429; I2 = 79%) groups. Results for AB, RC, and DC were statistically significant. The AB achieved the highest specificity (Sp) (mean 0.9889, 95% CI 0.9736-1; I2 = 0%), followed by RC (mean 0.9724, 95% CI 0.9431-1; I2 = 79%), DC (mean 0.9516, 95% CI 0.9116 0.9917; I2 = 90%), N (mean 0.9226, 95% CI 0.8385-1; I2 = 95%) and OKC (mean 0.8991, 95% CI 0.8683-0.9298; I2 = 8%) groups. DC, N, and RC had statistically significant results. Diagnostic odds ratios (DOR) showed that classification was better than chance for all lesion types. Conclusions: AI demonstrated high specificity, and is therefore effective in identifying healthy individuals. However, its sensitivity in detecting diseased patients remains suboptimal and requires further improvement.

permanent tooth, or enucleation of the cyst along with the removal of involved tooth, or use of a marsupialization technique. 4his case reports the occurrence of dentigerous cyst at a relatively rare location and associated management by saving the developing permanent tooth.

CK7 and P63 as predictive markers for neoplastic transformation of dentigerous and glandular odontogenic cysts to mucoepidermoid carcinoma.

An automated diagnostic support system for jaw pathologies on panoramic radiographs: a DenseNet121-CBAM deep learning study with histopathological correlation.

Dentigerous cyst in maxillary sinus: two-year follow-up

This study aims to provide Deep Learning (DL) based artificial intelligence (AI) methods using histopathology images to diagnose different types of odontogenic cysts (OCs) differentially. Within the scope of the proposed study, hematoxylin and eosin (H&E) stained images of 3 different cyst groups were used. The dataset consists of histopathology images of 87 Dentigerous cysts (DC), 198 radicular cyst (RC), and 63 odontogenic keratocyst (OKC). Each image was zoomed with 3 different zoom levels and resized to 224x224 as preprocessing. In addition to the classical CNN method, Inception V3, VGG16, VGG19, and Xception architectures were used. The data set was split into training, validation, and test groups to avoid retesting. The average accuracy, precision, sensitivity (recall), and F1-Score values obtained for CNN were 0.77, 0.80, 0.77, 0.75, and for VGG16 were 0.89, 0.90, 0.89, 0.89. For VGG19, these values were determined as 0.89, 0.90, 0.89, and 0.88, for Xception, these values were determined as 0.62, 0.52, 0.62,and 0.52 and for Inception, these values were determined as 0.62, 0.62, 0.62 and 0.56. It was observed that VGG16 and VGG19 models showed superior performance on the data set in question, while Xception and Inception V3 models converged slower, meaning the training process progressed slower. Results showed that deep neural networks can be efficiently used in detecting OCs. AI-based OC detection may be a decision support tool that reduces interprofessional variability, expedites the diagnostic process, and lessens clinician workload.

This case report presents a rare instance of Cleidocranial Dysplasia (CCD) in a 27-year-old male exhibiting multiple dentigerous cysts, a feature seldom documented in literature. The patient exhibited hallmark CCD features including short stature, drooping shoulders with hypermobility, frontal bossing, midface deficiency, and mandibular prognathism. Intraoral findings included multiple missing permanent teeth, retained deciduous teeth, and gingival swellings. Radiographic evaluation using orthopantomogram and CBCT revealed multiple impacted teeth, supernumerary teeth, open cranial sutures, and cystic lesions associated with impacted teeth. A multidisciplinary treatment plan was formulated involving surgical enucleation of cysts, orthodontic traction for eruption guidance, and prosthetic rehabilitation. This case emphasizes the importance of early diagnosis and coordinated multidisciplinary management in addressing the complex skeletal and dental manifestations of CCD to improve functional and esthetic outcomes.

Introduction Accurate interpretation of radiographic images is crucial for general dentists in making diagnoses and treatment decisions for their patients, yet the reliability of their diagnoses is often uncertain. This study evaluates the diagnostic efficacy of the differential diagnosis software ORADIII (Oral Radiology Artificial Intelligence Diagnostic - Version 3) in interpreting jaw lesions from cone-beam computed tomography (CBCT) scans, comparing its performance to that of an oral and maxillofacial radiologist. Materials and methods A total of 100 CBCT cases were selected from a clinical archive between the years 2013 and 2023, focusing on patients with jaw lesions. Among these, 85 cases were confirmed by biopsy reports. The top three differential diagnoses provided by ORADIII were analyzed, and their diagnostic accuracy was compared with that of an oral and maxillofacial radiologist who utilized clinical information and 3D-rendering software (Invivo; Anatomage Inc., Santa Clara, CA, USA). Statistical analyses were performed to evaluate the significance of differences in diagnostic accuracy between the two methods. Results The accuracy of ORADIII was 21% when compared to the oral radiologist's diagnoses, which achieved an accuracy of 68% against a definitive biopsy. ORADIII accurately diagnosed dentigerous cysts in five out of six instances. However, statistical analysis revealed a significant difference at the 0.05 level, indicating that the oral radiologist's top differential diagnosis was more accurate than ORADIII's. Conclusion While ORADIII demonstrates potential as an adjunct tool for general dentists in diagnosing jaw lesions, it should not be relied upon as a standalone solution. The study underscores the importance of clinical expertise in achieving accurate diagnoses and highlights the need for further research to enhance ORADIII's algorithms by incorporating additional clinical data and exploring machine-learning techniques for improved diagnostic accuracy. This study reinforces the complementary roles of technology and clinical judgment in the field of dentistry.

Odontogenic cysts constitute a common group of lesions affecting the jaws and represent a relevant clinical and epidemiological burden in oral and maxillofacial practice. Their occurrence varies across populations, and understanding global prevalence patterns is essential for improving diagnosis, treatment planning, and public health strategies. However, an updated synthesis of prevalence data across different regions remains limited. A systematic review was conducted following a predefined methodology, with a comprehensive search of PubMed, EBSCOhost, and Web of Science for studies published between 2000 and 2024. Eligible studies included observational designs (retrospective and and cross-sectional studies) that reported the prevalence of odontogenic cysts according to the WHO classification, with sufficient demographic and diagnostic data. Case reports, narrative reviews, and studies with small sample sizes or incomplete datasets were excluded. The PRISMA 2020 guidelines were applied to ensure transparent and complete reporting of the review process. Risk of bias was independently assessed using the Newcastle-Ottawa Scale (NOS) tailored for prevalence and frequency studies. Of 445 records initially identified, 17 studies comprising large institutional and population-based samples met the inclusion criteria after screening and quality assessment. The studies originated from multiple continents, with retrospective designs predominating. The calculated global prevalence of odontogenic cysts was 13.8%. Radicular cysts were the most prevalent subtype, followed by dentigerous cysts. Risk of bias assessment indicated that most studies demonstrated low to moderate risk, primarily related to retrospective design and variability in diagnostic reporting. The findings demonstrate marked geographic variability in the prevalence and distribution of odontogenic cysts, underscoring the influence of diagnostic practices and reporting standards. These results highlight the need for standardized classification, uniform reporting protocols, and high-quality epidemiological studies to enhance comparability across regions and support evidence-based clinical and public health decision-making.

Odontogenic keratocyst (OKC) is a cystic lesion with locally aggressive behavior and high recurrence rates, making treatment decisions complex. Conservative approaches have been proposed to preserve oral structures; however, few cases have reported their association with bone reconstruction. This report describes a pediatric case of extensive mandibular OKC managed conservatively with associated bone regeneration. An 11-year-old female patient presented with a radiographic finding of an extensive asymptomatic intraosseous lesion in the right mandible. Incisional biopsy and decompression device placement were performed, with initial diagnosis of dentigerous cyst. After six months, the device was replaced by a smaller one to promote bone neoformation. Nine months later, a second surgery was performed with device removal, extraction of teeth 47 and 48, and lesion enucleation. Histopathology confirmed OKC. At 28 months postoperatively, recurrence adjacent to the right first molar was detected, and a new conservative procedure was carried out, incorporating bone grafting with sticky bone. The patient was followed up for six months, showing absence of recurrence and graft healing. Despite initial misdiagnosis and multiple surgeries, conservative management preserved the stomatognathic system and avoided ablative procedures. Postoperative follow-up enabled early detection of recurrence and timely intervention associated with bone reconstruction.

This study tested how many patients receiving treatment for pericoronal radiolucencies were confirmed to have dentigerous cysts and also assessed factors associated with the prevalence of dentigerous cysts. A retrospective chart review study of 62 patients was conducted, including panoramic radiographs, pathology reports, demographic variables, and clinical data. In total, 53.2% of study patients were diagnosed with dentigerous cysts. A statistically significant correlation was found between gender and the presence of dentigerous cysts, but not between age and a diagnosis of dentigerous cysts. Unilocular/multilocular lesions and the number of involved teeth also presented statistically significant correlations to this diagnosis. This study demonstrates that dentigerous cysts are the most common type of pericoronal radiolucencies and highlights the importance of radiographic and clinical data in diagnosis.

The glandular odontogenic cyst is now a well-known entity comprising &lt; 0.5% of all odontogenic cysts with a recent review tabulating about 200 cases in the English literature. Glandular odontogenic cyst shows epithelial features that mimic glandular differentiation. The importance of glandular odontogenic cyst relates to the fact that it has a high recurrence rate and shares overlapping histologic features with central mucoepidermoid carcinoma. Glandular odontogenic cyst shows no pathognomonic clinico -radiographic characteristics and therefore in many cases it resembles a wide spectrum of jaw cysts and malignancies. Most of the times diagnosis can be difficult due to histopathological similarities with dentigerous cyst, lateral periodontal cyst and central mucoepidermoid carcinoma. Therefore, careful histopathological examination and a long-term follow-up are required to rule out recurrences.

Background: Dentigerous cysts are the most frequent developmental odontogenic cyst, typically found in relation to unerupted mandibular third molars or maxillary canines and rare in the first decade of life. Ectopic migration of the third molar in the maxillary sinus to the level of orbital floor is an uncommon presentation, particularly in children. Case Presentation: A 10-year-old boy presented to the outpatient department of our institution with a swelling in the right hemiface of one year's duration. Computed tomography (CT) scan demonstrated a well-circumscribed unilocular expansile lesion (3.7 × 2.9 × 2.8 cm) in the right maxillary sinus with displacement of the root of an upper lateral incisor and an ectopic third molar displaced to the orbital floor. Management: Surgical enucleation with widening of the tooth-bone window using a sublabial (Caldwell–Luc) approach including extraction of ectopic and impacted teeth was carried out. Histopathology confirmed a dentigerous cyst. Conclusion: This case illustrates that dentigerous cysts can cause marked tooth displacement in children and underlines the need for precise early diagnosis using CT to avoid orbital and sinus sequelae.

Jaw cysts are a diverse group of intraosseous lesions commonly encountered in oral and maxillofacial pathologies. Several studies have addressed their distribution and clinicopathological features; however, further large-scale analyses using standardized classification systems may enhance cross-regional comparability and diagnostic consistency. We aimed to evaluate the demographic and anatomical characteristics of jaw cysts over an almost 50-year period. We retrospectively reviewed 19,352 histologically confirmed jaw cysts diagnosed between 1975 and 2024. Each case was reclassified according to the 2022 WHO classifications for head and neck tumors. Patient age, sex, cyst type, and anatomical location data were collected and descriptively analyzed to identify trends across different cyst categories. Odontogenic cysts comprised the majority of cases. Radicular cysts were the most common, followed by dentigerous cysts and odontogenic keratocysts. Non-odontogenic cysts mainly consisted of surgical ciliated cysts and nasopalatine duct cysts. Bone cysts (simple bone cysts and aneurysmal bone cysts), which lack an epithelial lining and were therefore analyzed separately, were infrequent. A male predominance was observed overall, with sex- and age-related patterns differing according to cyst type. Mandibular involvement was more common than maxillary involvement, and several cysts showed specific anatomical predilections. These findings highlight distinct demographic and anatomical characteristics across cyst categories. This large retrospective study provides a detailed epidemiological profile of jaw cysts. The findings revealed distinct patterns according to cyst type, age, sex, and anatomical site, providing a valuable reference for diagnostic refinement and future comparative studies of oral and maxillofacial pathology.