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Occurrence of dermoid cyst in the floor of the mouth: the importance of differential diagnosis in pediatric patients.

ABSTRACT: Lesions in the floor of the mouth can be a challenging diagnosis due to the variety of pathological conditions that might be found in this area. Within a broad range of lesions, attention has to be addressed to those that require specific management, such as a dermoid cyst (DC) and a ranula. Especially in pediatric patients, in whom the failure of diagnosis can postpone the correct treatment and cause sequelae later in life. DC, a developmental anomaly, is managed primarily by surgical resection. On the other hand, ranula is a pseudocyst that may be treated by marsupialization. This article reports a large and painful lesion in the floor of the mouth in a pediatric patient. With a diagnostic hypothesis of ranula, two surgical interventions were performed, but there were recurrences of the lesion. Subsequently, the patient was referred to the Oral and Maxillofacial Surgery Unit for re-evaluation. Computed tomography showed a semi-transparent image suggesting a cystic formation. Another surgical procedure was performed where the lesion was completely removed. Anatomopathological analysis confirmed the diagnosis of DC. The five-year follow-up showed no signs of recurrence. This article indicates that although DC in the floor of the mouth is rare, it should be considered in the differential diagnosis of other diseases in this area. This precaution may be particularly important in the following circumstances: 1) Similar lesions that have different therapeutic approaches and, 2) To prevent future sequelae in pediatric patients.

SUBMITTER: Puricelli E

PROVIDER: S-EPMC5482258 | biostudies-other | 2017 May-Jun

REPOSITORIES: biostudies-other

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Project description:BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963-1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967-1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.

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Occurrence of dermoid cyst in the floor of the mouth: the importance of differential diagnosis in pediatric patients.

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