OBJECTIVE: To assess the value of chromosomal microarray analysis (CMA) for the diagnosis of fetuses with anomalies of the central nervous system (CNS) and summarize the outcome of the pregnancies and follow-up. METHODS: A total of 636 fetuses from June 2014 to December 2020 who were referred to the Prenatal Diagnosis Center of Nanjing Drum Tower Hospital due to abnormal CNS prompted by ultrasound were selected as the research subjects. Based on the ultrasound findings, the fetuses were divided into ventricular dilatation group (n = 441), choroid plexus cyst group (n = 41), enlarged posterior fossa group (n = 42), holoprosencephaly group (n = 15), corpus callosum hypoplasia group (n = 22), and other anomaly group (n = 75). Meanwhile, they were also divided into isolated (n = 504) and non-isolated (n = 132) groups based on the presence of additional abnormalities. Prenatal samples (amniotic fluid/chorionic villi/umbilical cord blood) or abortus tissue were collected for the extraction of genomic DNA and CMA assay. Outcome of the pregnancies and postnatal follow-up were summarized and subjected to statistical analysis. RESULTS: In total 636 fetuses with CNS anomalies (including 89 abortus tissues) were included, and 547 cases were followed up. The overall detection rate of CMA was 11.48% (73/636). The detection rates for the holoprosencephaly group, ACC group, choroid plexus cyst group, enlarged posterior fossa group, ventricular dilatation group and other anomaly group were 80% (12/15), 31.82% (7/22), 19.51% (8/41), 14.29% (6/42), 7.48% (33/441) and 9.33% (7/75), respectively. Compared with the isolated CNS anomaly group, the detection rate for the non-isolated CNS anomaly group was significantly higher (6.35% vs. 31.06%) (32/504 vs. 41/132) (χ² = 62.867, P < 0.001). Follow up showed that, for 52 fetuses with abnormal CMA results, 51 couples have opted induced labor, whilst 1 was delivered at full term with normal growth and development. Of the 434 fetuses with normal CMA results, 377 were delivered at full term (6 had developmental delay), and 57 couples had opted induced labor. The rate of adverse pregnancy outcome for non-isolated CNS abnormal fetuses was significantly higher than that of isolated CNS abnormal fetuses (26.56% vs. 10.54%) (17/64 vs. 39/370) (χ² = 12.463, P < 0.001). CONCLUSION Fetuses with CNS anomaly should be tested with CMA to determine the genetic cause. Most fetuses with negative CMA result have a good prognosis, but there is still a possibility for a abnormal neurological phenotype. Fetuses with CNS abnormalities in conjunct with other structural abnormalities are at increased risk for adverse pregnancy outcomes.
Female ; Pregnancy ; Humans ; Holoprosencephaly ; Prenatal Diagnosis/methods* ; Central Nervous System ; Fetus/abnormalities* ; Nervous System Malformations/genetics* ; Microarray Analysis ; Central Nervous System Diseases ; Cysts ; Chromosome Aberrations ; Ultrasonography, Prenatal/methods*

Laryngeal cyst is a cystic lesion occurring in the laryngeal cavity. Large laryngeal cyst in infants and young children can cause laryngeal wheezing and other upper airway obstruction symptoms. In severe cases, it can be even life-threatening and requires timely surgical treatment. Currently, there is a lack of unified clinical treatment strategy for this disease.This article summarizes the surgical methods, the advantages and disadvantages of various surgical methods for laryngeal cysts in recent years. It is recommended that needle aspiration, partial cyst wall resection, radical cyst dissection, transoral robotic surgery or external approach cyst resection should be selected through full communication and evaluation to clarify the extent of the lesion scope and the advantages and disadvantages of surgery.
Infant ; Child ; Humans ; Child, Preschool ; Cysts/diagnosis* ; Laryngeal Diseases/diagnosis* ; Larynx/surgery* ; Robotic Surgical Procedures ; Biopsy, Needle

Malignant transformation arising in benign lymphoepithelial cysts is a complex and rare occurrence, and related research is limited. This study presents a case of the malignant degeneration of lymphoepithelial cyst in parapharyngeal space. Clinicopathological features and differential diagnosis are discussed with literature review to provide reference for clinical diagnosis and treatment management.
Humans ; Carcinoma ; Diagnosis, Differential ; Cysts

There is a high incidence of chronic periapical periodontitis of deciduous teeth, however, there is a low incidence of the apical cyst. This paper reports a 7-year-old child with deciduous periodontitis caused by chronic periapical periodontitis of deciduous teeth. Through literature review, the etiology, imaging characteristics, diagnosis, differential diagnosis, and treatment methods were discussed to provide the basis for clinical diagnosis and treatment.
Child ; Humans ; Cysts ; Diagnosis, Differential ; Periapical Periodontitis/therapy* ; Tooth, Deciduous

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

The diagnosis of cystic liver disease has made great progress with the advent of enhanced imaging techniques. At the same time, its management has gradually improved over the past few decades, providing the basis for the development of appropriate diagnostic and treatment guidelines. To this end, the European Association for the Study of the Liver has developed clinical guidelines for the diagnosis and treatment of non-infectious cystic liver disease. This guideline put forward recommendations based on an in-depth review of the relevant literature for addressing clinical issues, including the diagnosis and treament of hepatic cysts, hepatic mucocystic tumors, biliary hamartomas, polycystic liver disease, Caroli disease or Caroli syndrome, biliary hamartomas, and peribiliary cyst.
Humans ; Liver Diseases/pathology* ; Cysts/pathology* ; Caroli Disease/diagnosis* ; Liver Neoplasms/therapy* ; Hamartoma

Peripheral odontogenic keratocysts are rarely observed, and cases of odontogenic keratocysts of buccal soft tissues are even rarer. This study was performed to present two rare cases of odontogenic keratocysts in buccal soft tissues and review related literature.
Humans ; Odontogenic Cysts/diagnosis* ; Odontogenic Tumors

The application of artificial intelligence in medicine has gradually received attention along with its development. Many studies have shown that machine learning has a wide range of applications in stomatology, especially in the clinical diagnosis and treatment of maxillofacial cysts and tumors. This article reviews the application of machine learning in maxillofacial cyst and tumor to provide a new method for the diagnosis of oral and maxillofacial diseases.
Artificial Intelligence ; Cysts/diagnosis* ; Humans ; Machine Learning ; Oral Medicine

OBJECTIVES: This study aimed to determine the prevalence of odontogenic cysts, tumors, and other lesions among reports in the archives of the Department of Oral and Maxillofacial Surgery at the Faculty of Dentistry affiliated with Kocaeli University collected over a four-year period.MATERIALS AND METHODS: In this retrospective study, patient records from the archive of the Department of Oral and Maxillofacial Surgery from 2014 to 2018 were reviewed. Patient demographic information (age and sex) and lesion location were recorded and analyzed.RESULTS: From a total of 475 files reviewed, odontogenic cyst was confirmed in 340 cases (71.6%), and odontogenic tumor was confirmed in 52 cases (10.9%). Regarding odontogenic cyst type, the most common was radicular cyst (216 cases), followed by dentigerous cyst (77 cases) and odontogenic keratocyst (23 cases). Among odontogenic tumors, the most frequent was odontoma (19 cases), followed by ossifying fibroma (18 cases) and ameloblastoma (9 cases). Giant cell granuloma was also reported in 35 cases.CONCLUSION: The distribution pattern of odontogenic cysts and tumors in our retrospective study is relatively similar to that reported in the literature. Complete clinical reports for final diagnosis of these lesions and routine follow-up examinations are very important for treatment.
Ameloblastoma ; Archives ; Biopsy ; Dentigerous Cyst ; Dentistry ; Diagnosis ; Fibroma, Ossifying ; Follow-Up Studies ; Granuloma, Giant Cell ; Humans ; Jaw Neoplasms ; Odontogenic Cysts ; Odontogenic Tumors ; Odontoma ; Prevalence ; Radicular Cyst ; Retrospective Studies ; Surgery, Oral

PURPOSE: The purpose of this study was to investigate the feasibility of shear wave ultrasound elastography for differentiating superficial benign soft tissue masses through a comparison of their shear moduli. METHODS: We retrospectively analyzed 48 masses from 46 patients from February 2014 to May 2016. Surgical excision, fine-needle aspiration, and clinical findings were used for the differential diagnosis. The ultrasonographic examinations were conducted by a single musculoskeletal radiologist, and the ultrasonographic findings were reviewed by two other radiologists who were blinded to the final diagnosis. Conventional ultrasonographic features and the median shear modulus were evaluated. We compared the median shear moduli of epidermoid cysts, ganglion cysts, and lipomatous tumors using the Kruskal-Wallis test. Additionally, the Mann-Whitney U test was used to compare two distinct groups. RESULTS: Significant differences were found in the median shear moduli of epidermoid cysts, ganglion cysts, and lipomatous tumors (23.7, 5.8, and 9.2 kPa, respectively; P=0.019). Epidermoid cysts showed a greater median shear modulus than ganglion cysts (P=0.014) and lipomatous tumors (P=0.049). CONCLUSION: Shear wave elastography may contribute to the differential diagnosis of superficial benign soft tissue masses through a direct quantitative analysis.
Biopsy, Fine-Needle ; Diagnosis ; Diagnosis, Differential ; Elastic Modulus ; Elasticity Imaging Techniques* ; Epidermal Cyst ; Ganglion Cysts ; Humans ; Lipoma ; Retrospective Studies ; Shear Strength ; Ultrasonography