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Cureus Oct 2023A radicular cyst, also known as a periapical cyst or root end cyst, is a type of odontogenic cyst that is typically associated with permanent teeth. The radicular cyst...
A radicular cyst, also known as a periapical cyst or root end cyst, is a type of odontogenic cyst that is typically associated with permanent teeth. The radicular cyst usually is associated with maxillary central incisors followed by mandibular first molars. It occurs as a result of bacterial infection and pulpal necrosis which leads to inflammatory stimulation of the epithelial cell rests of Malassez along the periodontal ligament area of the tooth. Most cases of the radicular cyst are asymptomatic and they are diagnosed accidentally during routine radiographic examination. This article presents a case report of a 42-year-old male with an apical periodontal cyst associated with the maxillary anterior region. Early diagnosis and treatment planning is necessary. This article signifies the role of the surgeon in the early diagnosis and treatment plan of the cyst.
PubMed: 37965401
DOI: 10.7759/cureus.47030 -
Expression of Calretinin Expression in Odontogenic Cysts and Odontogenic Tumors - Original Research.Journal of Pharmacy & Bioallied Sciences Feb 2024The present study was conducted for assessing variability in calretinin expression among odontogenic cysts as well as tumor cases.
AIM
The present study was conducted for assessing variability in calretinin expression among odontogenic cysts as well as tumor cases.
MATERIALS AND METHODS
Fifteen cases were included in the present research consisting of cases like - dentigerous cyst, odontogenic keratocyst, apical radicular cyst along with tumors like ameloblastoma, ameloblastic carcinoma, adenomatoid odontogenic tumor. Calretinin antibody was used for immunohistochemical staining. The amount of expression of this calretinin was statistically analyzed with the help of Chi-square test where < 0.05 was considered noteworthy statistically.
RESULTS
Most cases of ameloblastomas were highly positive for calretinin expression as compared to other cysts and tumors. Therefore, the correlation of this variation of expression of calretinin was statistically noteworthy ( = 0.00).
CONCLUSION
In this study, we concluded that for ameloblastomas, calretinin can be a specific marker immunohistochemically and can help in identifying the amount of aggressive spread of various odontogenic tumors.
PubMed: 38595345
DOI: 10.4103/jpbs.jpbs_469_23 -
JPMA. the Journal of the Pakistan... Apr 2024We present a case of nasopalatine duct cyst in a 35-yearold female. The cyst was diagnosed based on the presence of only one clinical symptom and no obvious clinical...
We present a case of nasopalatine duct cyst in a 35-yearold female. The cyst was diagnosed based on the presence of only one clinical symptom and no obvious clinical signs, which is a relatively rare occurrence. However, the radiographic and histological presentation of this lesion was typical of a nasopalatine duct cyst. Therefore, this case report aims to highlight the variable presentations of the nasopalatine cyst, which is often misdiagnosed and treated as an endodontic infection.
Topics: Humans; Female; Adult; Nonodontogenic Cysts; Diagnosis, Differential; Nose Diseases; Cysts; Palate, Hard
PubMed: 38751287
DOI: 10.47391/JPMA.9934 -
Journal of Oral Pathology & Medicine :... Sep 2023Odontogenic keratocysts constitute 10%-20% of odontogenic cysts and exhibit a distinctive corrugated parakeratinized lining epithelium. Considering that cornified...
BACKGROUND
Odontogenic keratocysts constitute 10%-20% of odontogenic cysts and exhibit a distinctive corrugated parakeratinized lining epithelium. Considering that cornified envelope formation is an important phenomenon during keratinocyte differentiation, this study aimed to clarify the characteristics of cornified envelope formation in odontogenic keratocysts.
METHODS
We investigated the cellular distribution of cornified envelope-related proteins (transglutaminases and their substrates), as well as the upstream regulatory protein c-Fos, by immunohistochemical analysis of the lining epithelium of 20 odontogenic keratocysts. We examined the corresponding mRNA levels by quantitative polymerase chain reaction. Ten dentigerous cysts served as control non-keratinized cysts.
RESULTS
The distributions of transglutaminase and their substrates except loricrin and small protein-rich protein 1a significantly differed between odontogenic keratocysts and dentigerous cysts. There was no significant difference in c-Fos expression between odontogenic keratocysts and dentigerous cysts. The mRNA levels of transglutaminases and their substrates were significantly higher in odontogenic keratocysts than in dentigerous cysts. However, c-Fos mRNA levels did not significantly differ between groups.
CONCLUSION
Surprisingly, the overall appearance of cornified envelope-related proteins of odontogenic keratocysts was consistent with the characteristics of non-keratinized oral mucosa identified in previous studies. These findings indicate that the contribution of cornified envelope-related molecules in odontogenic keratocysts is similar to that in non-keratinized oral epithelium, rather than keratinized oral epithelium, suggesting that odontogenic keratocysts are not genuine keratinized cysts. The upregulation of cornified envelope-related genes in odontogenic epithelium could be an important pathognomonic event during odontogenic keratocyst development.
Topics: Humans; Dentigerous Cyst; Odontogenic Cysts; Epithelium; Transglutaminases
PubMed: 37438940
DOI: 10.1111/jop.13464 -
Journal of Dentistry Aug 2023Dentists and oral surgeons often face difficulties distinguishing between radicular cysts and periapical granulomas on panoramic imaging. Radicular cysts require...
OBJECTIVES
Dentists and oral surgeons often face difficulties distinguishing between radicular cysts and periapical granulomas on panoramic imaging. Radicular cysts require surgical removal while root canal treatment is the first-line treatment for periapical granulomas. Therefore, an automated tool to aid clinical decision making is needed.
METHODS
A deep learning framework was developed using panoramic images of 80 radicular cysts and 72 periapical granulomas located in the mandible. Additionally, 197 normal images and 58 images with other radiolucent lesions were selected to improve model robustness. The images were cropped into global (affected half of the mandible) and local images (only the lesion) and then the dataset was split into 90% training and 10% testing sets. Data augmentation was performed on the training dataset. A two-route convolutional neural network using the global and local images was constructed for lesion classification. These outputs were concatenated into the object detection network for lesion localization.
RESULTS
The classification network achieved a sensitivity of 1.00 (95% C.I. 0.63-1.00), specificity of 0.95 (0.86-0.99), and AUC (area under the receiver-operating characteristic curve) of 0.97 for radicular cysts and a sensitivity of 0.77 (0.46-0.95), specificity of 1.00 (0.93-1.00), and AUC of 0.88 for periapical granulomas. Average precision for the localization network was 0.83 for radicular cysts and 0.74 for periapical granulomas.
CONCLUSIONS
The proposed model demonstrated reliable diagnostic performance for the detection and differentiation of radicular cysts and periapical granulomas. Using deep learning, diagnostic efficacy can be enhanced leading to a more efficient referral strategy and subsequent treatment efficacy.
CLINICAL SIGNIFICANCE
A two-route deep learning approach using global and local images can reliably differentiate between radicular cysts and periapical granulomas on panoramic imaging. Concatenating its output to a localizing network creates a clinically usable workflow for classifying and localizing these lesions, enhancing treatment and referral practices.
Topics: Humans; Periapical Granuloma; Radicular Cyst; Deep Learning; Radiography; Neural Networks, Computer
PubMed: 37295547
DOI: 10.1016/j.jdent.2023.104581 -
Oral Surgery, Oral Medicine, Oral... Mar 2024To evaluate the diagnostic capability of artificial intelligence (AI) for detecting and classifying odontogenic cysts and tumors, with special emphasis on odontogenic... (Review)
Review
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.
PubMed: 38845306
DOI: 10.1016/j.oooo.2024.03.004 -
Academic Radiology Oct 2023This study aimed to investigate the reliability and accuracy of high-resolution ultrasonography (US) for diagnosing periapical lesions and differentiating radicular...
RATIONALE AND OBJECTIVES
This study aimed to investigate the reliability and accuracy of high-resolution ultrasonography (US) for diagnosing periapical lesions and differentiating radicular cysts from granulomas.
MATERIALS AND METHODS
This study included 109 teeth with periapical lesions of endodontic origin from 109 patients scheduled for apical microsurgery. Ultrasonic outcomes were analyzed and categorized after thorough clinical and radiographic examinations using US. B-mode US images reflected the echotexture, echogenicity, and lesion margin, while color Doppler US assessed the presence and features of blood flow of interested areas. Pathological tissue samples were obtained during apical microsurgery and subjected to histopathological examination. Fleiss' κ was used to measure interobserver reliability. Statistical analyses were performed to assess the diagnostic validity and the overall agreement between US and histological findings. The reliability of US compared to histopathological examinations was assessed based on Cohen's κ.
RESULTS
The percent accuracy of US for diagnosing cysts, granulomas, and cysts with infection based on histopathological findings was 89.9%, 89.0%, and 97.2%, respectively. The sensitivity of US diagnoses was 95.1% for cysts, 84.1% for granulomas, and 80.0% for cysts with infection. The specificity of US diagnoses was 86.8% for cysts, 95.7% for granulomas, and 98.1% for cysts with infection. The reliability for US compared to histopathological examinations was good (κ = 0.779).
CONCLUSION
The echotexture characteristics of lesions in US images correlated with their histopathological features. US can provide accurate information on the nature of periapical lesions based on the echotexture of their contents and the presence of vascularity. It can help improve clinical diagnosis and avoid overtreatment of patients with apical periodontitis.
Topics: Humans; Radicular Cyst; Periapical Granuloma; Reproducibility of Results; Granuloma; Ultrasonography
PubMed: 37394410
DOI: 10.1016/j.acra.2023.05.039 -
The Saudi Dental Journal Dec 2023Non-endodontic lesions (NEL) closely resemble lesions of endodontic origin. Its etiology can be odontogenic, non-odontogenic, neoplastic, or anatomic variations that can... (Review)
Review
Non-endodontic lesions (NEL) closely resemble lesions of endodontic origin. Its etiology can be odontogenic, non-odontogenic, neoplastic, or anatomic variations that can resemble inflammatory periapical lesions in the periapical area. Inflammatory periapical lesions are caused by pulpal pathoses and require endodontic treatment. Since numerous NEL may resemble inflammatory periapical lesions, they can lead to misdiagnosis and inappropriate management. Thus, a detailed review of the patients' medical and dental histories with clinical examination, including radiographic findings, is essential for the proper assessment of periapical lesions. Numerous cases of misdiagnoses of NEL have been reported in literature. Thus, this review aimed to strengthen the awareness of clinicians on periapical radiolucency, which may resemble inflammatory periapical lesions.
PubMed: 38107039
DOI: 10.1016/j.sdentj.2023.11.003 -
Oral Radiology Jul 2024The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near similar presentation, anatomical variations, and superimposition. It is crucial that the performance of these models is evaluated for their clinical applicability in diagnosing cysts and tumors.
METHODS
A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT.
RESULTS
16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier.
CONCLUSION
The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.
Topics: Humans; Machine Learning; Odontogenic Cysts; Odontogenic Tumors; Sensitivity and Specificity; Cone-Beam Computed Tomography
PubMed: 38530559
DOI: 10.1007/s11282-024-00745-7 -
Journal of Cancer Research and... Jan 2024Glandular odontogenic cyst (GOC) is a rare developmental odontogenic cyst from the cell rests of Serres. GOC is locally aggressive with a tendency toward recurrence. The... (Review)
Review
Glandular odontogenic cyst (GOC) is a rare developmental odontogenic cyst from the cell rests of Serres. GOC is locally aggressive with a tendency toward recurrence. The most common site of occurrence is the anterior mandible with an asymptomatic presentation. Radiographically, it presents as unilocular or multilocular radiolucency. It bears histopathological resemblance to low-grade mucoepidermoid carcinoma. We report two cases of GOC occurring in a 16-year-old and a 33-year-old male patient with a review of the clinical presentation, histopathological features, and diagnostic aspects of GOC reported so far in literature.
Topics: Adult; Humans; Male; Carcinoma, Mucoepidermoid; Mandible; Odontogenic Cysts; Adolescent
PubMed: 38554373
DOI: 10.4103/jcrt.jcrt_2344_22