The efficacy of CBCT-based radiomics techniques in differentiating between conventional and unicystic ameloblastoma.

OBJECTIVE: The aim of this study was to develop a cone beam computed tomography (CBCT) radiomics-based model that differentiates between conventional and unicystic ameloblastoma (AB). METHODS: In this retrospective study, CBCT images were collected from 100 patients who had ABs that were diagnosed histopathologically as conventional or unicystic AB after surgical treatment. The patients were randomly divided into training (70) and validation (30) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into 5 models: Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, Random Forest, and XGBoost for prediction of tumor type. Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA). RESULTS: The 20 optimal radiomics features were incorporated into the Logistic Regression (LR) model, which exhibited the best overall performance with AUC = 0.936 (95% confidence interval [CI] = 0.877-0.996) for the training cohort and AUC = 0.929 (95% CI = 0.832-1.000) for the validation cohort. The nomogram combined the clinical features and the radiomics signature and resulted in the best predictive performance. CONCLUSIONS: The LR model demonstrated the ability of radiomics and the nomogram to distinguish between the 2 types of AB and may have the potential to replace biopsies under noninvasive conditions.

Unicystic ameloblastoma: Clinico-radiological and histopathological correlation with management.

Unicystic ameloblastoma is a distinct entity of ameloblastoma characterized by slow growth and locally aggressive behavior. This retrospective study aimed to assess the efficacy of different treatment modalities of unicystic ameloblastoma, focusing on clinico-radiological and histopathological features. Data from patients diagnosed with unicystic ameloblastoma were retrospectively analyzed. Patients were categorized into luminal and intraluminal (Group A) and mural (Group B) variants based on the Ackermann classification, which has a significant influence on their biological behavior, treatment approaches, and prognosis. Patients in Group A underwent enucleation with chemical cauterization, peripheral ostectomy, and iodoform packing, whereas those in Group B were treated with resection and reconstruction. Post-operatively, the patients were subjected to radiographic assessments via digital orthopantomogram at regular intervals. Because of the rarity of unicystic ameloblastoma, only 17 patients were included in the study (Group A: 9 patients; Group B: 8 patients), with a mean follow-up of 4.9 years (range: 1.4-11.8 years). The primary outcome measure was the absence of recurrence, which indicated treatment success. No patient in either group experienced recurrence within the follow-up period. This study provides evidence supporting the successful treatment of luminal and intraluminal variants of unicystic ameloblastoma in young individuals using a conservative approach. However, the more aggressive mural variant demonstrated favorable outcomes with radical treatment. These findings emphasize the importance of the Ackermann classification in guiding treatment decisions for unicystic ameloblastoma and contribute valuable insights into optimizing therapeutic strategies based on clinico-radiological and histopathological findings.

A three-dimensional quantitative assessment on bony growth and symmetrical recovery of mandible after decompression for unicystic ameloblastoma.

Unicystic ameloblastoma (UAM) of the jaw can be effectively reduced in volume through decompression, which promotes bone regeneration and restores jaw symmetry. This study quantitatively evaluated changes in mandible volume and symmetry following decompression of mandibular UAM. This study included 17 patients who underwent surgical decompression followed by second-stage curettage for mandibular UAM. Preoperative and postoperative three-dimensional computed tomography (CT) images were collected. Bone volume and the area of cortical perforation were measured to assess bone growth during decompression. Mandibular volumetric symmetry was analyzed by calculating the volumetric ratio of the two sides of the mandible. Twelve pairs of landmarks were identified on the surface of the lesion regions, and their coordinates were used to calculate the mean asymmetry index (AI) of the mandible. Paired t-tests and the Mann-Whitney U test were used for statistical analysis, with p < 0.05 considered indicative of statistical significance. The mean duration of decompression was 9.41 ± 3.28 months. The mean bone volume increased by 8.07 ± 2.41%, and cortical perforation recovery was 71.97 ± 14.99%. The volumetric symmetry of the mandible improved significantly (p < 0.05), and a statistically significant decrease in AI was observed (p < 0.05). In conclusion, UAM decompression enhances bone growth and symmetry recovery of the mandible. The present evaluation technique is clinically useful for quantitatively assessing mandibular asymmetry.

Diagnostic capability of artificial intelligence tools for detecting and classifying odontogenic cysts and tumors: a systematic review and meta-analysis.

OBJECTIVE: To evaluate the diagnostic capability of artificial intelligence (AI) for detecting and classifying odontogenic cysts and tumors, with special emphasis on odontogenic keratocyst (OKC) and ameloblastoma. STUDY DESIGN: Nine electronic databases and the gray literature were examined. Human-based studies using AI algorithms to detect or classify odontogenic cysts and tumors by using panoramic radiographs or CBCT were included. Diagnostic tests were evaluated, and a meta-analysis was performed for classifying OKCs and ameloblastomas. Heterogeneity, risk of bias, and certainty of evidence were evaluated. RESULTS: Twelve studies concluded that AI is a promising tool for the detection and/or classification of lesions, producing high diagnostic test values. Three articles assessed the sensitivity of convolutional neural networks in classifying similar lesions using panoramic radiographs, specifically OKC and ameloblastoma. The accuracy was 0.893 (95% CI 0.832-0.954). AI applied to cone beam computed tomography produced superior accuracy based on only 4 studies. The results revealed heterogeneity in the models used, variations in imaging examinations, and discrepancies in the presentation of metrics. CONCLUSION: AI tools exhibited a relatively high level of accuracy in detecting and classifying OKC and ameloblastoma. Panoramic radiography appears to be an accurate method for AI-based classification of these lesions, albeit with a low level of certainty. The accuracy of CBCT model data appears to be high and promising, although with limited available data.

A radiologic-pathologic study of the histopathologic variants of ameloblastomas and their proliferation indices.

OBJECTIVES: This study aimed to analyze the clinicoradiologic features and Ki-67 proliferation indices between the histopathologic variants of ameloblastomas (ABs) for possible associations. STUDY DESIGN: The diagnosis and histopathologic variant were confirmed for all cases by experienced Oral and Maxillofacial Pathologists. Immunohistochemistry for Ki-67 was performed on the most representative formalin-fixed paraffin-embedded tissue block. Demographic, clinical data and radiologic features were analyzed from patient records and available radiographic examinations. The investigators were blinded to the histopathologic variant and proliferation index when the clinicoradiologic features were assessed. RESULTS: The current study included 116 cases of AB in the final sample. The indolent behavior of the unicystic variant was supported by their low proliferation index and slow growth paired with low frequencies of cortical destruction, loss of teeth, root resorption, and encroachment on anatomical structures. In contrast, the comparatively high proliferation index of the plexiform variant correlated with their fast growth and pain. Furthermore, high radiologic frequencies of cortical destruction, loss of teeth, and encroachment of surrounding anatomical structures supported their more aggressive clinical course. CONCLUSION: Statistically significant differences were noted between certain variants and Ki-67, location, borders, locularity, and cortical destruction, providing better insight into their biological behavior.

Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: A systematic review.

BACKGROUND: The purpose of this systematic review (SR) is to gather evidence on the use of machine learning (ML) models in the diagnosis of intraosseous lesions in gnathic bones and to analyze the reliability, impact, and usefulness of such models. This SR was performed in accordance with the PRISMA 2022 guidelines and was registered in the PROSPERO database (CRD42022379298). METHODS: The acronym PICOS was used to structure the inquiry-focused review question "Is Artificial Intelligence reliable for the diagnosis of intraosseous lesions in gnathic bones?" The literature search was conducted in various electronic databases, including PubMed, Embase, Scopus, Cochrane Library, Web of Science, Lilacs, IEEE Xplore, and Gray Literature (Google Scholar and ProQuest). Risk of bias assessment was performed using PROBAST, and the results were synthesized by considering the task and sampling strategy of the dataset. RESULTS: Twenty-six studies were included (21 146 radiographic images). Ameloblastomas, odontogenic keratocysts, dentigerous cysts, and periapical cysts were the most frequently investigated lesions. According to TRIPOD, most studies were classified as type 2 (randomly divided). The F1 score was presented in only 13 studies, which provided the metrics for 20 trials, with a mean of 0.71 (±0.25). CONCLUSION: There is no conclusive evidence to support the usefulness of ML-based models in the detection, segmentation, and classification of intraosseous lesions in gnathic bones for routine clinical application. The lack of detail about data sampling, the lack of a comprehensive set of metrics for training and validation, and the absence of external testing limit experiments and hinder proper evaluation of model performance.

The 'D-M-C' strategy for conventional ameloblastoma of the mandible: a retrospective study.

The purpose of this multicentre study was to evaluate the efficacy of the 'dredging-marsupialization-curettage' (D-M-C) strategy in the treatment of conventional intraosseous ameloblastoma of the mandible. A total of 31 patients from three institutions, who had a pathological diagnosis of conventional ameloblastoma of the mandible, were treated with the D-M-C strategy. The surgical protocol comprised a dredging and marsupialization (D-M) step, with additional D-M steps as required. The patients then underwent curettage (C) once an obvious effect of the D-M step had been achieved during follow-up. Eight patients were followed up for ≥36 months but <60 months, while 23 were followed up for ≥60 months. Nineteen of the 23 patients followed up for ≥60 months were disease-free at the last follow-up, with no evidence of recurrence. The D-M step is effective for reducing the tumour size and preserving vital structures. The D-M-C surgical strategy may be a feasible treatment option for conventional ameloblastoma of the mandible.

Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT.

OBJECTIVES: Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image features for the preoperative differential diagnosis of AME and OKC and compares ML algorithms to expert radiologists to validate performance. METHODS: We retrospectively collected the data of 326 patients with AME and OKC, where all diagnoses were confirmed by histopathologic tests. A total of 348 features were selected to train six ML models for differential diagnosis by a 5-fold cross-validation. We then compared the performance of ML-based diagnoses to those of radiologists. RESULTS: Among the six ML models, XGBoost was effective in distinguishing AME and OKC in CBCT images, with its classification performance outperforming the other models. The mean precision, recall, accuracy, F1-score, and area under the curve (AUC) were 0.900, 0.807, 0.843, 0.841, and 0.872, respectively. Compared to the diagnostics by radiologists, ML-based radiomic diagnostics performed better. CONCLUSIONS: Radiomic-based ML algorithms allow CBCT images of AME and OKC to be distinguished accurately, facilitating the preoperative differential diagnosis of AME and OKC. ADVANCES IN KNOWLEDGE: ML and radiomic approaches with high-resolution CBCT images provide new insights into the differential diagnosis of AME and OKC.

Automatic segmentation of ameloblastoma on ct images using deep learning with limited data.

BACKGROUND: Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures. METHODS: We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance. RESULTS: During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance. CONCLUSION: In this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment.

Dentigerous cysts suspected the other odontogenic lesions on panoramic radiography and CT.

Dentigerous cysts are known as the second most common type of cyst in the jaws. The cyst is one of the lesions occurred frequently in the posterior body of the mandible and is often related to the unerupted third molar and forms around the crown of the unerupted tooth attaching at the cementoenamel junction. Such characteristic appearances are the diagnostic points differentiating from ameloblastoma or odontogenic keratocyst. However, it would be hard for us to diagnose it as a dentigerous cyst if the lesion does not show its typical appearance. We experienced two cases of dentigerous cysts which did not form around the crown of the unerupted tooth on radiologically. Both cysts were relatively large and resorbed adjacent teeth roots. Therefore, an ameloblastoma or an odontogenic keratocyst was suspected rather than a dentigerous cyst as the imaging diagnosis. The biopsy revealed that the lesion was a "dentigerous cyst" in one of the cases and "developmental cyst with inflammation" in another case. After the excision, the histopathological diagnosis was a dentigerous cyst with inflammation in both cases. This report shows the two cases of dentigerous cysts focusing on panoramic radiography and CT images. Also, we discuss the differential diagnosis by reconsidering those diagnostic points.

Clinicopathologic Profile and Surgical Modalities in Mandibular Ameloblastoma: A Descriptive Study.

BACKGROUND: Ameloblastoma is a benign neoplasm composed of epithelial tissue with invasive and infiltrative behavior at the local level and a high recurrence rate, with various histopathologic patterns and clinical forms. Approximately 85% of conventional ameloblastomas occur in the mandible, most often in the body, angle, and ascending ramus area. The treatment modalities include both conservative and radical treatments. Postoperative follow-up is most important in the treatment of ameloblastoma. AIMS AND OBJECTIVES: To describe the clinicopathologic profile of mandibular ameloblastoma in patients undergoing different surgical modalities. The primary objective was to describe the clinicopathologic profile and surgical management of mandibular ameloblastoma in patients aged ≥18 years, who had reported to a tertiary dental care center for follow-up during the study period. The secondary objective was to describe the distribution of comorbidities associated with different surgical modalities and reconstructive methods. SUBJECTS AND METHODS: A total of 34 patients with mandibular ameloblastoma who underwent various surgical modalities between 2011 and 2021 were studied. Information was collected using a predesigned proforma and statistically analyzed. RESULTS: Thirty-four review cases of ameloblastoma were included in the study. The patients were analyzed concerning age, sex, site, size, clinical presentation, radiographic pattern, histopathologic subtype, type of surgery, and associated comorbidities. Most cases of mandibular ameloblastoma involve the age of 16 to 55 years. The mean age of occurrence was found to be 35.5±13.2. A female preponderance, a tumor size range of 2 to 4 cm, a multicystic variant, involvement of the mandibular body in the premolar-molar area, root resorption, cortical perforation, and a follicular type of histopathologic pattern were the common presentations. Isolated anterior tumors restricted to the incisor/canine region were not found. The common surgical modalities undertaken were conservative methods such as enucleation, and chemical cauterization, and radical methods such as marginal mandibulectomy and segmental resection. Reconstruction using a titanium plate or free fibular graft was performed in the indicated cases. The common comorbidities included difficulty in chewing and loss of facial contour. Recurrence after surgical treatment was rare. Only 9% of cases developed a recurrence within 5 years. No recurrence was noted in cases treated with radical treatment, whereas 50% of cases treated with conservative methods showed recurrence. CONCLUSION: The age of occurrence, site, and size of the tumor, cortical perforation, root resorption, histopathologic type, and radiographic patterns are widely considered factors in devising a treatment plan for mandibular ameloblastoma. However, there may be rare instances where these tumors behave differently regardless of their innocuous clinicopathologic presentation. Surgical procedures such as segmental resection and marginal mandibulectomy were found to be promising for the eradication of the tumor, and prevention of recurrences and metastasis. However, conservative measures such as enucleation and chemical cauterization were fraught with an increased risk of tumor recurrence and metastasis. Future studies with a larger sample size should focus on the clinicopathologic characteristics of ameloblastoma to elucidate its varied behavior and develop newer and advanced treatment modalities that would provide better surgical and postsurgical outcomes in affected patients.

Peripheral ameloblastoma underlying squamous cell papilloma after a third molar extraction.

Peripheral ameloblastoma (PA) is a rare variant of ameloblastoma that presents as a slow-growing, painless mass in the gingival tissues or alveolar mucosa. It shares histologic features with conventional ameloblastoma but is less invasive and aggressive. This case report describes a 51-year-old female with a PA that simultaneously or subsequently developed underlying squamous cell papilloma after mandibular third molar extraction. Clinical examination revealed a pedunculated gingival lesion mimicking squamous cell papilloma. Histopathologic examination confirmed PA underlying squamous cell papilloma after an excisional biopsy. Imaging revealed mild bone resorption, leading to a further soft tissue excision and minimal osteoectomy to rule out intraosseous involvement. The patient remained asymptomatic without signs of recurrence in the 1-year follow-up. PA diagnosis can be challenging due to its clinical resemblance to other gingival lesions and histopathologic features. Treatment typically involves surgical excision, with long-term follow-up recommended due to possible recurrence and malignant transformation.

Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas.

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.

[Cone-beam CT imaging features of common cystic lesions associated with the impacted mandibular third molar].

Objective: To analyze the imaging features of cone-beam CT (CBCT) of ameloblastoma (AB), odontogenic keratocyst (OKC) and dentigerous cysts (DC) associated with the mandibular impacted third molars,so as to provide useful information for differential diagnosis of these lesions. Methods: The patients who were with complete clinical data, pathological diagnosis and CBCT images from AB, OKC and DC around the mandibular impacted third molars were collected in Peking University Hospital of Stomatology from August 2016 to December 2021. A total of 109 patients (14 were diagnosed as AB, 23 were diagnosed as OKC and the others were diagnosed as dental cysts) were collected, including 73 males and 36 females. The age ranged from 11 to 70. The analyzed imaging features included location and internal density of the lesions, bone expansion, root resorption of adjacent teeth and types of the impacted teeth. The Chi square test was used to compare the gender of different lesions, and the Fisher's exact test was used to compare imaging features of lesions. When P<0.05, there was a significant difference among the three. Logistic regression analysis was performed to determine the imaging features that significantly contribute to correct imaging diagnosis. Corresponding P-values were calculated for all factors from multivariate models. Results: In the 23 cases of OKC, no special location was observed for the center of the lesion, heterogeneous high-density were seen in 21.7% of the cases, 56.5% of the cases had no significant bone expansion and the impacted teeth were not specially oriented. Among the 14 AB, 7 cases (7/14) were mainly located in the ramus of the mandible, and all cases (14/14) had buccal/lingual expansion of the jaw, 8 cases (8/14) presented root resorption of the adjacent teeth, and mesial impacted mandibular third molar were seen in 6 cases (6/14). Among the 72 DC, 88.9% (64/72) of the cases were mainly limited to the crown of the impacted third molar, 72.2% (52/72) of the cases had no obvious bone expansion, inverted impacted teeth were shown in 56.9% (41/72) of the cases. There was a significant difference among the three groups (χ2=7.30, P=0.026) in gender. AB and odontogenic cyst were more common in men than in women, while the incidence of OKC was roughly equal between men and women.There were significant differences in the location (P<0.001), internal density (P=0.001) of the lesions, bone expansion (P<0.001) and types of the impacted teeth (P<0.001), while no statistical difference was found for root resorption of adjacent teeth (P=0.153). Logistics regression analysis showed that the location of the lesion, internal density, bone expansion, root resorption of adjacent teeth and the types of impacted teeth had significant effects on the accurate diagnosis of the three kinds of lesions. Conclusions: Location, internal density, bone expansion and types of the impacted teeth played an important role in the correct imaging diagnosis. Further analysis indicates that when the classification of impacted teeth and the location of lesions are considered synchronously, DC can be differentiated from AB and OKC.

Machine learning-based radiomics for predicting BRAF-V600E mutations in ameloblastoma.

Background: Ameloblastoma is a locally invasive and aggressive epithelial odontogenic neoplasm. The BRAF-V600E gene mutation is a prevalent genetic alteration found in this tumor and is considered to have a crucial role in its pathogenesis. The objective of this study is to develop and validate a radiomics-based machine learning method for the identification of BRAF-V600E gene mutations in ameloblastoma patients. Methods: In this retrospective study, data from 103 patients diagnosed with ameloblastoma who underwent BRAF-V600E mutation testing were collected. Of these patients, 72 were included in the training cohort, while 31 were included in the validation cohort. To address class imbalance, synthetic minority over-sampling technique (SMOTE) is applied in our study. Radiomics features were extracted from preprocessed CT images, and the most relevant features, including both radiomics and clinical data, were selected for analysis. Machine learning methods were utilized to construct models. The performance of these models in distinguishing between patients with and without BRAF-V600E gene mutations was evaluated using the receiver operating characteristic (ROC) curve. Results: When the analysis was based on radiomics signature, Random Forest performed better than the others, with the area under the ROC curve (AUC) of 0.87 (95%CI, 0.68-1.00). The performance of XGBoost model is slightly lower than that of Random Forest, and its AUC is 0.83 (95% CI, 0.60-1.00). The nomogram evident that among younger women, the affected region primarily lies within the mandible, and patients with larger tumor diameters exhibit a heightened risk. Additionally, patients with higher radiomics signature scores are more susceptible to the BRAF-V600E gene mutations. Conclusions: Our study presents a comprehensive radiomics-based machine learning model using five different methods to accurately detect BRAF-V600E gene mutations in patients diagnosed with ameloblastoma. The Random Forest model's high predictive performance, with AUC of 0.87, demonstrates its potential for facilitating a convenient and cost-effective way of identifying patients with the mutation without the need for invasive tumor sampling for molecular testing. This non-invasive approach has the potential to guide preoperative or postoperative drug treatment for affected individuals, thereby improving outcomes.

Unicystic ameloblastoma - A case series.

Ameloblastoma is a benign, locally aggressive neoplasm that constitutes about 1-3% of the tumors of the jaw. Wide surgical excision with adequate safe margin is the most common treatment of choice. The study aimed to manage cases with unicystic ameloblastoma while preserving the continuity of the mandible (without resection). This article presents a series of cases ranging from 18 to 40 years old patients of both sexes with unicystic ameloblastoma, especially in the mandible showing more male predilection than female. All the cases presented in this article were treated by enucleation and curettage. None of the patients presented post-operative paresthesia. None of the cases went in for resection. Post-operative recovery was uneventful in all the patients. All the patients were followed up for a period of 3.5-5 years. None of the cases reported recurrence at the date of publication.

Mandibular reconstruction after excision of recurrent odontogenic keratocyst using a novel mandibular distraction osteogenesis method- a case report.

BACKGROUND: Odontogenic keratocyst is one of the most common benign odontogenic neoplasms with a high recurrence rate. Its resection has the potential to lead to mandibular segmental defects. In this case report, we describe a patient with odontogenic keratocyst who underwent radical resection using a novel distraction osteogenesis (DO) method to reconstruct mandibular segmental defect. CASE PRESENTATION: This case report describes a 19-year-old woman with odontogenic keratocyst of the mandible that recurred after multiple curettages and eventually necessitated radical resection. Mandibular segmental defect after radical resection was reconstructed using a novel DO method that involved directly contacting the segment ends of the defect without the transport disk. However, the distractor broke during the retention period, and a molding titanium plate was used for fixation. This novel distraction method achieved mandibular reconstruction and restored mandibular function and contour.

Dentinogenic ghost cell tumor associated with odontoma: report of a rare case and review of literature.

Dentinogenic ghost cell tumor (DGCT) is an uncommon odontogenic tumor considered to be the solid, tumorous variant of calcifying odontogenic cyst. DGCT is characterized by islands of ameloblastoma-like epithelial cells that resemble the enamel organ, the presence of ghost cells, and dentinoid material. This article reports a rare case of dentinogenic ghost cell tumor associated with an odontoma in an adult patient, with a review of the literature. To the best of the authors' knowledge, there have only been four case reports of DGCT associated with odontoma, all of which occurred in children and adults younger than 30 years old.

[Application of Deep Learning in Differential Diagnosis of Ameloblastoma and Odontogenic Keratocyst Based on Panoramic Radiographs].

Objective To evaluate the accuracy of different convolutional neural networks (CNN),representative deep learning models,in the differential diagnosis of ameloblastoma and odontogenic keratocyst,and subsequently compare the diagnosis results between models and oral radiologists. Methods A total of 1000 digital panoramic radiographs were retrospectively collected from the patients with ameloblastoma (500 radiographs) or odontogenic keratocyst (500 radiographs) in the Department of Oral and Maxillofacial Radiology,Peking University School of Stomatology.Eight CNN including ResNet (18,50,101),VGG (16,19),and EfficientNet (b1,b3,b5) were selected to distinguish ameloblastoma from odontogenic keratocyst.Transfer learning was employed to train 800 panoramic radiographs in the training set through 5-fold cross validation,and 200 panoramic radiographs in the test set were used for differential diagnosis.Chi square test was performed for comparing the performance among different CNN.Furthermore,7 oral radiologists (including 2 seniors and 5 juniors) made a diagnosis on the 200 panoramic radiographs in the test set,and the diagnosis results were compared between CNN and oral radiologists. Results The eight neural network models showed the diagnostic accuracy ranging from 82.50% to 87.50%,of which EfficientNet b1 had the highest accuracy of 87.50%.There was no significant difference in the diagnostic accuracy among the CNN models (P=0.998,P=0.905).The average diagnostic accuracy of oral radiologists was (70.30±5.48)%,and there was no statistical difference in the accuracy between senior and junior oral radiologists (P=0.883).The diagnostic accuracy of CNN models was higher than that of oral radiologists (P<0.001). Conclusion Deep learning CNN can realize accurate differential diagnosis between ameloblastoma and odontogenic keratocyst with panoramic radiographs,with higher diagnostic accuracy than oral radiologists.