The odontogenic keratocyst (OKC) is a developmental odontogenic cyst typically occurring in the jaws. Since the first description of OKC was published in 1956, the lesion has been of particular interest because of its specific histopathologic features, high recurrence rate, and aggressive behavior. Recurrences most commonly arise within bone at the site of the original cyst. However, as lining cells may find their way into surrounding tissues either from implantation during surgery or from cortical perforation recurrences may arise at a distance from the original cyst. Here, we report a rare case of recurrent OKC which was first developed in mandible and recurred within the masticatory space.
Diagnostic Imaging ; Jaw ; Mandible ; Masticatory Muscles ; Odontogenic Cysts ; Recurrence

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.
Humans ; Ameloblastoma/diagnostic imaging* ; Deep Learning ; Diagnosis, Differential ; Radiography, Panoramic ; Retrospective Studies ; Odontogenic Cysts/diagnostic imaging* ; Odontogenic Tumors

Odontogenic keratocyst (OKC) with secondary inflammation involving the maxillary sinus was presented. Radiological diagnosis of this case was made based on the various findings from the cone-beam computed tomography, computed tomography and magnetic resonance images. There were calcified materials and impacted tooth within the lumen of the lesion, which is not uncommon in OKC. Histopathologic findings confirmed this lesion as OKC with secondary inflammation.
Calcinosis ; Cone-Beam Computed Tomography ; Diagnostic Imaging ; Inflammation ; Magnetic Resonance Spectroscopy ; Maxilla ; Maxillary Sinus ; Odontogenic Cysts ; Tooth, Impacted