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Cureus May 2024A radicular cyst is characterized as an odontogenic cyst of inflammatory origin that develops from Malassez epithelial rests in the periodontal ligament as the...
A radicular cyst is characterized as an odontogenic cyst of inflammatory origin that develops from Malassez epithelial rests in the periodontal ligament as the consequence of dental pulp inflammation. The cyst commenced in the carious tooth and spread to the periodontal and periapical regions. The majority of these lesions appear as precise radiolucencies and encompass their entire apex. The cystic lesion, which is also called a root-end cyst or periapical cyst, is sometimes referred to as a true cyst because it is lined by fluid epithelium. There are several treatment options to address radicular cysts, including surgical and nonsurgical methods. In this case study, we described the clinical observation of the cyst. The cyst typically manifests in later life due to its prolonged etiology. The maxillary anterior region is the most frequently utilized site.
PubMed: 38872665
DOI: 10.7759/cureus.60269 -
Metabolomics : Official Journal of the... Feb 2024Odontogenic keratocysts (OKCs) are locally aggressive and have a high rate of recurrence, but the pathogenesis of OKCs is not fully understood. We aimed to investigate...
INTRODUCTION
Odontogenic keratocysts (OKCs) are locally aggressive and have a high rate of recurrence, but the pathogenesis of OKCs is not fully understood. We aimed to investigate the serum metabolomic profile of OKCs and discover potential biomarkers.
METHODS
Metabolomic analysis was performed on 42 serum samples from 22 OKC patients and 20 healthy controls (HCs) using gas chromatography‒mass spectrometry to identify dysregulated metabolites in the OKC samples. LASSO regression and receiver operating characteristic (ROC) curve analyses were used to select and validate metabolic biomarkers and develop diagnostic models.
RESULTS
A total of 73 metabolites were identified in the serum samples, and 24 metabolites were dysregulated in the OKC samples, of which 4 were upregulated. Finally, a diagnostic panel of 10 metabolites was constructed that accurately diagnosed OKCs (sensitivity of 100%, specificity of 100%, area under the curve of 1.00).
CONCLUSION
This study is the first to investigate the metabolic characteristics and potential metabolic biomarkers in the serum of OKC patients using GC‒MS. Our study provides further evidence to explore the pathogenesis of OKC.
Topics: Humans; Metabolomics; Odontogenic Cysts; Biomarkers; Gas Chromatography-Mass Spectrometry; ROC Curve
PubMed: 38416246
DOI: 10.1007/s11306-024-02101-6 -
Orphanet Journal of Rare Diseases Jul 2023Primary cardiac tumors in children are very rare and may be associated with severe arrhythmias and sudden infant death syndrome. These cardiac arrhythmias vary depending... (Review)
Review
Primary cardiac tumors in children are very rare and may be associated with severe arrhythmias and sudden infant death syndrome. These cardiac arrhythmias vary depending on the location and size of the tumor. Sixty-four percent of children with cardiac fibroma, the second most common benign cardiac tumor in children, have ventricular arrhythmias, affecting therapeutic management and risk profile of these children. We report on two siblings with cardiac fibromas whose clinical presentations differed depending on their locations and size of the tumors. The first child, a three-year-old girl, was diagnosed with a cardiac fibroma in the left ventricle at the age of 8 months after surviving resuscitation due to ventricular fibrillation. Secondary prophylactic implantation of an ICD was performed. On propranolol, no further malignant arrhythmias have occurred to date. The seven-month-old brother was diagnosed postnatally with a cardiac tumor adjacent to the right ventricle. A few weeks after birth, the boy had refractory supraventricular tachycardia and ventricular arrhythmia that only resolved with amiodarone. In genetic testing, Gorlin-Goltz syndrome was diagnosed in both children. Conservative pharmacological therapy is a therapeutic strategy for asymptomatic patients with cardiac fibromas. The anti-arrhythmic medication depends on the location of the tumor. Implantation of an ICD should be performed in cases of malignant arrhythmias. In rare cases, there is an association between cardiac tumors and genetic syndromes, such as Gorlin-Goltz syndrome. These should always be considered when such a tumor is diagnosed.
Topics: Male; Child; Infant; Female; Humans; Child, Preschool; Siblings; Tachycardia, Ventricular; Basal Cell Nevus Syndrome; Arrhythmias, Cardiac; Heart Neoplasms; Fibroma
PubMed: 37408081
DOI: 10.1186/s13023-023-02792-5 -
International Journal of Oral Science Feb 2024Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental...
Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898-0.973) and prognosis (AUC = 0.840, 95%CI: 0.751-0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.
Topics: Humans; Artificial Intelligence; Diagnosis, Differential; Odontogenic Cysts; Basal Cell Nevus Syndrome; Odontogenic Tumors; Prognosis
PubMed: 38403665
DOI: 10.1038/s41368-024-00287-y -
Imaging Science in Dentistry Dec 2023This study aimed to investigate the potential factors that could affect the reduction rate of odontogenic cysts following decompression using cone-beam computed...
PURPOSE
This study aimed to investigate the potential factors that could affect the reduction rate of odontogenic cysts following decompression using cone-beam computed tomography (CBCT) for 3-dimensional volumetric analysis.
MATERIALS AND METHODS
The study sample consisted of CBCT images of 41 individuals who underwent decompression of odontogenic cysts at the Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Chulalongkorn University, between 2010 and 2022. Preoperative and postoperative CBCT results were collected, and a volumetric analysis was conducted to evaluate the differences in the reduction rate and the percentage of volume reduction of cystic lesions based on different parameters. Correlations between these parameters were analyzed to determine associations.
RESULTS
In this study, the average time of decompression for odontogenic cysts was 316 days. Males demonstrated a higher reduction rate than females (<0.05). The reduction rate was directly proportional to initial cyst volume, with higher reduction rates for cysts with large initial volume than those with small initial volume (<0.05). Spearman's rank correlation coefficient indicated a weak positive correlation between the initial cyst volume and the duration of decompression. Additionally, a strong positive correlation was observed between the initial volume and the reduction rate.
CONCLUSION
Knowledge of the reduction rate of odontogenic cysts is vital for surgeons to evaluate the duration of decompression before enucleation and to determine a definitive treatment plan. Sex and initial lesion volume had significant effects on the reduction rate.
PubMed: 38174041
DOI: 10.5624/isd.20230083 -
Oral and Maxillofacial Surgery Dec 2023In this study, we prospectively investigated the relationship between bone marrow edema (BME) and odontogenic cysts and explored the possibility of using dual-energy...
PURPOSE
In this study, we prospectively investigated the relationship between bone marrow edema (BME) and odontogenic cysts and explored the possibility of using dual-energy computed tomography (DECT) as an auxiliary tool for the diagnosis of odontogenic cysts.
METHODS
This cross-sectional study included 73 patients who underwent the DECT scan and surgery for odontogenic cysts or odontogenic tumors. The virtual noncalcium (VNCa) computed tomography (CT) values and CT values were measured at several sites. The predictor variable was diagnosis, and the other variables included age, sex, and sites. The primary outcome was VNCa CT value. Variables were tested using the chi-square test or the Kruskal-Wallis test. The VNCa CT and CT values were tested using the Scheffe test for multiple comparisons. All variables were analyzed as independent variables affecting the VNCa CT values around the lesion in the multiple regression analysis.
RESULT
There were 35 men and 38 women. The mean patient age was 50.0 ± 19.5 years (range: 8-86). The VNCa CT values (- 6.2 ± 34.3) around the lesion in patients with RCs were significantly higher than those in patients with dentigerous cysts (- 44.4 ± 28.6) and odontogenic keratocysts (- 67.3 ± 19.5). In multiple regression analysis, the VNCa CT values around the lesion showed a significant positive correlation with histological results (regression coefficient: - 0.605, P < 0.001).
CONCLUSION
The presence of BME is associated with radicular cysts, and DECT can be used as an auxiliary tool for radicular cyst diagnosis.
Topics: Male; Humans; Female; Child; Adolescent; Young Adult; Adult; Middle Aged; Aged; Aged, 80 and over; Bone Marrow; Diagnosis, Differential; Cross-Sectional Studies; Magnetic Resonance Imaging; Bone Marrow Diseases; Edema; Odontogenic Cysts; Radicular Cyst; Sensitivity and Specificity
PubMed: 36121523
DOI: 10.1007/s10006-022-01113-7 -
Diagnostics (Basel, Switzerland) Nov 2023The microscopic diagnostic differentiation of odontogenic cysts from other cysts is intricate and may cause perplexity for both clinicians and pathologists. Of...
The microscopic diagnostic differentiation of odontogenic cysts from other cysts is intricate and may cause perplexity for both clinicians and pathologists. Of particular interest is the odontogenic keratocyst (OKC), a developmental cyst with unique histopathological and clinical characteristics. Nevertheless, what distinguishes this cyst is its aggressive nature and high tendency for recurrence. Clinicians encounter challenges in dealing with this frequently encountered jaw lesion, as there is no consensus on surgical treatment. Therefore, the accurate and early diagnosis of such cysts will benefit clinicians in terms of treatment management and spare subjects from the mental agony of suffering from aggressive OKCs, which impact their quality of life. The objective of this research is to develop an automated OKC diagnostic system that can function as a decision support tool for pathologists, whether they are working locally or remotely. This system will provide them with additional data and insights to enhance their decision-making abilities. This research aims to provide an automation pipeline to classify whole-slide images of OKCs and non-keratocysts (non-KCs: dentigerous and radicular cysts). OKC diagnosis and prognosis using the histopathological analysis of tissues using whole-slide images (WSIs) with a deep-learning approach is an emerging research area. WSIs have the unique advantage of magnifying tissues with high resolution without losing information. The contribution of this research is a novel, deep-learning-based, and efficient algorithm that reduces the trainable parameters and, in turn, the memory footprint. This is achieved using principal component analysis (PCA) and the ReliefF feature selection algorithm (ReliefF) in a convolutional neural network (CNN) named P-C-ReliefF. The proposed model reduces the trainable parameters compared to standard CNN, achieving 97% classification accuracy.
PubMed: 37958281
DOI: 10.3390/diagnostics13213384 -
Oral Surgery, Oral Medicine, Oral... Jul 2024The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and...
OBJECTIVE
The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs.
STUDY DESIGN
A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined.
RESULTS
The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs' with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively.
CONCLUSIONS
The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.
Topics: Humans; Radiography, Panoramic; Radicular Cyst; Neural Networks, Computer; Radiographic Image Interpretation, Computer-Assisted
PubMed: 38158267
DOI: 10.1016/j.oooo.2023.09.012 -
Iranian Journal of Otorhinolaryngology Nov 2023Tartrate-resistant acid phosphatase (TRAP) is an acid phosphatase metalloprotein enzyme expressed in osteoclasts and is related to bone resorption. The molecular...
INTRODUCTION
Tartrate-resistant acid phosphatase (TRAP) is an acid phosphatase metalloprotein enzyme expressed in osteoclasts and is related to bone resorption. The molecular mechanisms involved in the different behavior of odontogenic keratocysts have not yet been fully elucidated. The purpose of this study was to compare TRAP expression in odontogenic keratocysts, radicular cysts, and dentigerous cysts.
MATERIALS AND METHODS
In this cross-sectional study, we selected 60 samples, including 20 cases of each one of the odontogenic keratocysts (OKC), radicular cysts (RC) and dentigerous cysts (DC). The samples were stained with TRAP monoclonal antibodies using immunohistochemistry. The data were analyzed using the Chi-Square and Kruskal-Wallis tests.
RESULTS
In this study, TRAP expression was observed in the lining epithelium of 50% of OKC cases and 5% of RC cases, while it was negative in the lining epithelium of DC. This difference was statistically significant (p<0.001). Moreover, the TRAP staining intensity in the lining epithelium had a significant difference between the groups (P<0.001). TRAP expression in the connective tissue of OKC, RC, and DC was positive in 35%, 30%, and 20% of the cases, respectively. This difference was not statistically significant (P=0.788). Also, staining intensity of TRAP-positive cells in the connective tissue of the lesions was not significant (P=0.634).
CONCLUSION
In this study, we found a higher expression of TRAP in the lining epithelium of OKC, which may be one of the reasons for the aggressive behavior of OKC compared to other cysts. This finding supports the classification of OKC as an odontogenic tumor.
PubMed: 38074480
DOI: 10.22038/IJORL.2023.63350.3169 -
Medicina (Kaunas, Lithuania) Dec 2023: The purpose of this study was to develop and evaluate a deep learning model capable of autonomously detecting and segmenting radiolucent lesions in the lower jaw by...
: The purpose of this study was to develop and evaluate a deep learning model capable of autonomously detecting and segmenting radiolucent lesions in the lower jaw by utilizing You Only Look Once (YOLO) v8. : This study involved the analysis of 226 lesions present in panoramic radiographs captured between 2013 and 2023 at the Clinical Hospital Dubrava and the School of Dental Medicine, University of Zagreb. Panoramic radiographs included radiolucent lesions such as radicular cysts, ameloblastomas, odontogenic keratocysts (OKC), dentigerous cysts and residual cysts. To enhance the database, we applied techniques such as translation, scaling, rotation, horizontal flipping and mosaic effects. We have employed the deep neural network to tackle our detection and segmentation objectives. Also, to improve our model's generalization capabilities, we conducted five-fold cross-validation. The assessment of the model's performance was carried out through metrics like Intersection over Union (IoU), precision, recall and mean average precision (mAP)@50 and mAP@50-95. : In the detection task, the precision, recall, mAP@50 and mAP@50-95 scores without augmentation were recorded at 91.8%, 57.1%, 75.8% and 47.3%, while, with augmentation, were 95.2%, 94.4%, 97.5% and 68.7%, respectively. Similarly, in the segmentation task, the precision, recall, mAP@50 and mAP@50-95 values achieved without augmentation were 76%, 75.5%, 75.1% and 48.3%, respectively. Augmentation techniques led to an improvement of these scores to 100%, 94.5%, 96.6% and 72.2%. : Our study confirmed that the model developed using the advanced YOLOv8 has the remarkable capability to automatically detect and segment radiolucent lesions in the mandible. With its continual evolution and integration into various medical fields, the deep learning model holds the potential to revolutionize patient care.
Topics: Humans; Radiography, Panoramic; Mandible; Neural Networks, Computer; Odontogenic Cysts; Databases, Factual
PubMed: 38138241
DOI: 10.3390/medicina59122138