Oral manifestations of amyloidosis and the diagnostic applicability of oral tissue biopsy.

BACKGROUND: Amyloidosis exhibits a variable spectrum of systemic signs and oral manifestations that can be difficult to diagnose. This study aimed to characterize the clinical, demographic, and microscopic features of amyloidosis in the oral cavity. METHODS: This collaborative study involved three Brazilian oral pathology centers and described cases with a confirmed diagnosis of amyloidosis on available oral tissue biopsies. Clinical data were obtained from medical records. H&E, Congo-red, and immunohistochemically stained slides were analyzed. RESULTS: Twenty-six oral biopsies from 23 individuals (65.2% males; mean age: 59.6 years) were included. Oral involvement was the first sign of the disease in 67.0% of cases. Two patients had no clinical manifestation in the oral mucosa, although the histological analysis confirmed amyloid deposition. Amyloid deposits were distributed in perivascular (88.0%), periacinar and periductal (80.0%), perineurial (80.0%), endoneurial (33.3%), perimuscular (88.2%), intramuscular (94.1%), and subepithelial (35.3%) sites as well as around fat cells (100.0%). Mild/moderate inflammation was found in 65.4% of cases and 23.1% had giant cells. CONCLUSIONS: Amyloid deposits were consistently found in oral tissues, exhibiting distinct deposition patterns. Oral biopsy is less invasive than internal organ biopsy and enables the reliable identification of amyloid deposits even in the absence of oral manifestations. These findings corroborate the relevance of oral biopsy for the diagnosis of amyloidosis.

A primary oral mucosal melanoma of the hard palate with no recurrence: Report of a 10 years follow-up.

BACKGROUND: Primary oral mucosal melanoma (OMM) represents an extremely rare and aggressive tumor that arises from malignant transformation and clonal expansion of melanocytes in the oral cavity. The prognosis of patients affected by OMM is quite unfavorable, with survival rates lower than those described for patients with cutaneous melanoma. CASE REPORT: Here, we report a case of OMM in a 59-year-old Caucasian woman, who was referred for evaluation of a large asymptomatic pigmented lesion on the left side of the hard palate under the removable total denture. Incisional biopsy was performed, and histopathological analysis revealed the proliferation of spindle-shaped and pigmented epithelioid cells, with cellular pleomorphism. These cells were positive for Melan A, S-100, HMB-45, SOX-10, and Ki-67 confirming the diagnosis of OMM. The patient underwent partial maxillectomy and adjuvant radiotherapy. After treatment, she was rehabilitated with a palatal obturator prosthesis and has been in follow-up for 10 years with no evidence of disease. Due to the rarity in the oral cavity and the nonspecific signs and symptoms, the diagnosis of OMM is difficult and often overlooked. CONCLUSION: Therefore, multidisciplinary management from diagnosis, treatment, and rehabilitation is important to increase the expectation of cure.

Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas.

BACKGROUND: Odontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis. The main advantages of integrating Machine Learning (ML) with microscopic and radiographic imaging is the ability to significantly reduce intra-and inter observer variability and improve diagnostic objectivity and reproducibility. METHODS: Thirty Digitized slides were collected from different diagnostic centers of oral pathology in Brazil. After performing manual annotation in the region of interest, the images were segmented and fragmented into small patches. In the supervised learning methodology for image classification, three models (ResNet50, DenseNet, and VGG16) were focus of investigation to provide the probability of an image being classified as class0 (i.e., ameloblastoma) or class1 (i.e., Ameloblastic carcinoma). RESULTS: The training and validation metrics did not show convergence, characterizing overfitting. However, the test results were satisfactory, with an average for ResNet50 of 0.75, 0.71, 0.84, 0.65, and 0.77 for accuracy, precision, sensitivity, specificity, and F1-score, respectively. CONCLUSIONS: The models demonstrated a strong potential of learning, but lack of generalization ability. The models learn fast, reaching a training accuracy of 98%. The evaluation process showed instability in validation; however, acceptable performance in the testing process, which may be due to the small data set. This first investigation opens an opportunity for expanding collaboration to incorporate more complementary data; as well as, developing and evaluating new alternative models.

The use of deep learning state-of-the-art architectures for oral epithelial dysplasia grading: A comparative appraisal.

BACKGROUND: Dysplasia grading systems for oral epithelial dysplasia are a source of disagreement among pathologists. Therefore, machine learning approaches are being developed to mitigate this issue. METHODS: This cross-sectional study included a cohort of 82 patients with oral potentially malignant disorders and correspondent 98 hematoxylin and eosin-stained whole slide images with biopsied-proven dysplasia. All whole-slide images were manually annotated based on the binary system for oral epithelial dysplasia. The annotated regions of interest were segmented and fragmented into small patches and non-randomly sampled into training/validation and test subsets. The training/validation data were color augmented, resulting in a total of 81,786 patches for training. The held-out independent test set enrolled a total of 4,486 patches. Seven state-of-the-art convolutional neural networks were trained, validated, and tested with the same dataset. RESULTS: The models presented a high learning rate, yet very low generalization potential. At the model development, VGG16 performed the best, but with massive overfitting. In the test set, VGG16 presented the best accuracy, sensitivity, specificity, and area under the curve (62%, 62%, 66%, and 65%, respectively), associated with the higher loss among all Convolutional Neural Networks (CNNs) tested. EfficientB0 has comparable metrics and the lowest loss among all convolutional neural networks, being a great candidate for further studies. CONCLUSION: The models were not able to generalize enough to be applied in real-life datasets due to an overlapping of features between the two classes (i.e., high risk and low risk of malignization).

Development and validation of a multivariable model for prediction of malignant transformation and recurrence of oral epithelial dysplasia.

BACKGROUND: Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma which is amongst the top ten cancers worldwide. Prognostic significance of conventional histological features in OED is not well established. Many additional histological abnormalities are seen in OED, but are insufficiently investigated, and have not been correlated to clinical outcomes. METHODS: A digital quantitative analysis of epithelial cellularity, nuclear geometry, cytoplasm staining intensity and epithelial architecture/thickness is conducted on 75 OED whole-slide images (252 regions of interest) with feature-specific comparisons between grades and against non-dysplastic/control cases. Multivariable models were developed to evaluate prediction of OED recurrence and malignant transformation. The best performing models were externally validated on unseen cases pooled from four different centres (n = 121), of which 32% progressed to cancer, with an average transformation time of 45 months. RESULTS: Grade-based differences were seen for cytoplasmic eosin, nuclear eccentricity, and circularity in basal epithelial cells of OED (p < 0.05). Nucleus circularity was associated with OED recurrence (p = 0.018) and epithelial perimeter associated with malignant transformation (p = 0.03). The developed model demonstrated superior predictive potential for malignant transformation (AUROC 0.77) and OED recurrence (AUROC 0.74) as compared with conventional WHO grading (AUROC 0.68 and 0.71, respectively). External validation supported the prognostic strength of this model. CONCLUSIONS: This study supports a novel prognostic model which outperforms existing grading systems. Further studies are warranted to evaluate its significance for OED prognostication.

Histopathological parameters reported in microinvasive oral squamous cell carcinoma: a systematic review

Background: Microinvasive oral squamous cell carcinoma (OSCCmi) is an incipient stage of oral cancer. Through this systematic review, we aim to assess patterns of histopathological outcomes reported in OSCCmi cases. Material and methods: An online search in major databases was performed without period restriction, and 2,024 publications in English, Spanish and Portuguese were obtained. After screening and eligibility, 4 studies were selected. The risk of bias was assessed using Joanna Briggs Institute Critical Appraisal Checklist. A descriptive synthesis was conducted. Results: All 4 publications included were retrospective, reporting a total of 116 OSCCmi patients, with a male predominance (1.6:1) and a mean age of 55.9 years. The main parameters considered for microinvasion were tumor thickness (TT) (range 4-10mm) and depth of invasion (DOI) (range 0,02-5mm). Definition, cut-off values, and assessment of microscopic features were not standardized. Other relevant measures such as perineural or lymphovascular invasion and pattern of invasive front were barely described, and cytological/architectural characteristics were not discussed. Conclusions: TT and DOI are currently the primary histopathological criteria used to define OSCCmi. Nonetheless, the outcomes of this systematic review showed the absence of standardized quantitative parameters to render the diagnosis of microinvasive OSCC. Therefore, additional studies aiming to standardize histopathological features to diagnose OSCCmi are paramount. (AU)

Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis.

INTRODUCTION: The aim of the present systematic review (SR) is to summarize Machine Learning (ML) models currently used to predict head and neck cancer (HNC) treatment-related toxicities, and to understand the impact of image biomarkers (IBMs) in prediction models (PMs). The present SR was conducted following the guidelines of the PRISMA 2022 and registered in PROSPERO database (CRD42020219304). METHODS: The acronym PICOS was used to develop the focused review question (Can PMs accurately predict HNC treatment toxicities?) and the eligibility criteria. The inclusion criteria enrolled Prediction Model Studies (PMSs) with patient cohorts that were treated for HNC and developed toxicities. Electronic database search encompassed PubMed, EMBASE, Scopus, Cochrane Library, Web of Science, LILACS, and Gray Literature (Google Scholar and ProQuest). Risk of Bias (RoB) was assessed through PROBAST and the results were synthesized based on the data format (with and without IBMs) to allow comparison. RESULTS: A total of 28 studies and 4,713 patients were included. Xerostomia was the most frequently investigated toxicity (17; 60.71 %). Sixteen (57.14 %) studies reported using radiomics features in combination with clinical or dosimetrics/dosiomics for modelling. High RoB was identified in 23 studies. Meta-analysis (MA) showed an area under the receiver operating characteristics curve (AUROC) of 0.82 for models with IBMs and 0.81 for models without IBMs (p value < 0.001), demonstrating no difference among IBM- and non-IBM-based models. DISCUSSION: The development of a PM based on sample-specific features represents patient selection bias and may affect a model's performance. Heterogeneity of the studies as well as non-standardized metrics prevent proper comparison of studies, and the absence of an independent/external test does not allow the evaluation of the model's generalization ability. CONCLUSION: IBM-featured PMs are not superior to PMs based on non-IBM predictors. The evidence was appraised as of low certainty.

Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning.

OBJECTIVE: The present study aims to quantify clinicians' perceptions of oral potentially malignant disorders (OPMDs) when evaluating, classifying, and manually annotating clinical images, as well as to understand the source of inter-observer variability when assessing these lesions. The hypothesis was that different interpretations could affect the quality of the annotations used to train a Supervised Learning model. STUDY DESIGN: Forty-six clinical images from 37 patients were reviewed, classified, and manually annotated at the pixel level by 3 labelers. We compared the inter-examiner assessment based on clinical criteria through the κ statistics (Fleiss's kappa). The segmentations were also compared using the mean pixel-wise intersection over union (IoU). RESULTS: The inter-observer agreement for homogeneous/non-homogeneous criteria was substantial (κ = 63, 95% CI: 0.47-0.80). For the subclassification of non-homogeneous lesions, the inter-observer agreement was moderate (κ = 43, 95% CI: 0.34-0.53) (P < .001). The mean IoU of 0.53 (±0.22) was considered low. CONCLUSION: The subjective clinical assessment (based on human visual observation, variable criteria that have suffered adjustments over the years, different educational backgrounds, and personal experience) may explain the source of inter-observer discordance for the classification and annotation of OPMD. Therefore, there is a strong probability of transferring the subjectivity of human analysis to artificial intelligence models. The use of large data sets and segmentation based on the union of all labelers' annotations holds the potential to overcome this limitation.

Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review.

Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.

Diagnostic Accuracy of Liquid Biopsy for Oral Potentially Malignant Disorders and Head and Neck Cancer: an Overview of Systematic Reviews.

PURPOSE OF REVIEW: The aim of this overview is to appraise the evidence on salivary biomarkers for H&N cancer diagnosis. The acronym PICOS was used to develop the eligibility criteria and the focused review question: are liquid biopsies (saliva biomarkers) reliable for cancer detection in H&N cancer patients? Electronic database search encompassed PubMed, EMBASE, Scopus, Cochrane Library, Web of Science, and LILACS. Risk of Bias (RoB) was assessed through AMSTAR 2. RECENT FINDINGS: A total of 20 SRs were included. Only seven SRs were able to reach more solid conclusions around the retrieved findings by calculating the pooled sensitivity, specificity, and the overall area under the curve (AUC). Despite the limitations, significant RoB, and lack of test metrics in primary studies, all SRs recognize and encourage the potential role of saliva in the early diagnosis of oral cancer.

Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach.

INTRODUCTION: Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence-based diagnostic approaches, with a special focus on convolutional neural networks, the state-of-the-art in artificial intelligence and deep learning. METHODS: The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view. CONCLUSION: The development of artificial intelligence-based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction.

FGFR1 is an important prognostic factor in oral leukoplakia and tongue squamous cell carcinoma.

BACKGROUND: Fibroblast growth factor receptor 1 is a potential prognostic factor for tongue squamous cell carcinoma and is associated with oral epithelial dysplasia grade in oral leukoplakia. METHODS: Thirty cases of tongue squamous cell carcinoma and 30 cases of oral leukoplakia were analyzed. Fibroblast growth factor receptor 1 and phosphorylated Akt protein expression were analyzed by immunohistochemistry and quantified using a digital algorithm. Fibroblast growth factor receptor 1 gene amplification was analyzed by fluorescent in situ hybridization in the tongue squamous cell carcinoma cases. RESULTS: Clinical appearance and dysplasia grade were correlated with oral leukoplakia malignant transformation. Oral leukoplakia cases presenting high fibroblast growth factor receptor 1 expression showed a higher risk of malignant transformation (p = 0.016, HR: 7.3, 95% CI: 1.4-37.4). Phosphorylated Akt showed faint to no expression in oral leukoplakia, which did not correlate with dysplasia grade or malignant transformation. High expression of fibroblast growth factor receptor 1 and phosohorylated Akt were associated with poor overall survival and disease-free survival in tongue squamous cell carcinoma, although only fibroblast growth factor receptor 1 expression was significantly associated with poor overall survival (p = 0.024; HR: 4.9, 95% CI: 1.2-19.9). Cases presenting double fibroblast growth factor receptor 1/phosphorylated Akt overexpression (n = 8) showed markedly impaired overall survival (p = 0.020; HR: 6.4, 95% CI: 1.3-31.1) and disease-free survival (p = 0.001, HR: 13.0, 95% CI: 3.0-55.7). Fibroblast growth factor receptor 1 amplification was observed in 16.6% of tongue squamous cell carcinoma cases, being correlated with vascular and neural invasion (p = 0.001 and 0.017, respectively), but not with fibroblast growth factor receptor 1 protein expression, overall survival, or disease-free survival. CONCLUSION: Fibroblast growth factor receptor 1 protein expression is an important prognostic factor in oral leukoplakia and tongue squamous cell carcinoma.

Molecular profile of head and neck rhabdomyosarcomas: A systematic review and meta-analysis.

OBJECTIVE: This systematic review aimed to identify the molecular alterations of head and neck rhabdomyosarcomas (HNRMS) and their prognostic values. STUDY DESIGN: An electronic search was performed using PubMed, Embase, Scopus, and Web of Science with a designed search strategy. Inclusion criteria comprised cases of primary HNRMS with an established histopathological diagnosis and molecular analysis. Forty-nine studies were included and were appraised for methodological quality using the Joanna Briggs Institute Critical Appraisal tools. Five studies were selected for meta-analysis. RESULTS: HNRMS predominantly affects pediatric patients (44.4%), and the parameningeal region (57.7%) is the most common location. The alveolar variant (43.2%) predominates over the embryonal and spindle cell/sclerosing types, followed by the epithelioid and pleomorphic variants. PAX-FOXO1 fusion was observed in 103 cases of alveolar RMS (79.8%). MYOD1 mutation was found in 39 cases of sclerosing/spindle cell RMS (53.4%). FUS/EWSR1-TFCP2 gene fusions were identified in 21 cases of RMS with epithelioid and spindle cell morphologies (95.5%). The 5-year overall survival rate of patients was 61.3%, and MYOD1 mutation correlated with significantly higher mortality. CONCLUSION: The genotypic profile of histologic variants of HNRMS is widely variable, and MYOD1 mutation could be a potential prognostic factor, but more studies are required to establish this.

Impact of head and neck radiotherapy on the longevity of dental adhesive restorations: A systematic review and meta-analysis.

STATEMENT OF PROBLEM: Established restorative protocols for patients after head and neck radiotherapy are lacking, increasing the failure rates of dental adhesive restorations. PURPOSE: The purpose of this systematic review and meta-analysis was to analyze the evidence regarding the impact of head and neck radiotherapy on the longevity of dental adhesive restorations. MATERIAL AND METHODS: A search was performed using PubMed, Scopus, and Embase in May 2018 (updated in November 2020). Data extraction was performed regarding the percentage of restoration failure among dental adhesive materials, including glass ionomer cements, resin-modified glass ionomer cements, and composite resins. Risk of bias was assessed by the meta-analysis of statistics assessment and review instrument (MAStARI). Confidence in cumulative evidence was evaluated by the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) protocol. RESULTS: Four studies met the inclusion criteria. All included studies were classified as having a moderate risk of bias and reported results regarding class V restorations. Overall, composite resins presented lower failure rates at 2 years (30%) when compared with resin-modified glass ionomer (41%) and glass ionomer cements (57%). Meta-analysis showed that the risk of failure with glass ionomer cements was greater than with resin-modified glass ionomer cements (RR: 1.71, P<.001). Composite resins presented lower risk of failure when compared with glass ionomer (RR: 2.29, P<.001) and resin-modified glass ionomer cements (RR: 1.30, P=.03). Three studies reported results regarding fluoride compliance, which had a negative effect on the survival rates of glass ionomer and resin-modified glass ionomer cements and a positive effect on composite resin restorations. CONCLUSIONS: The results suggest that composite resin restorations associated with fluoride gel compliance seems to be the best alternative for restoring class V lesions in patients after head and neck radiotherapy. However, the results showed moderate certainty of evidence, which justifies the need for more randomized clinical trials regarding this subject.

Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results.

Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as "suspicious" and "normal" by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method's feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis.

Fully digital pathology laboratory routine and remote reporting of oral and maxillofacial diagnosis during the COVID-19 pandemic: a validation study.

The role of digital pathology in remote reporting has seen an increase during the COVID-19 pandemic. Recently, recommendations had been made regarding the urgent need of reorganizing head and neck cancer diagnostic services to provide a safe work environment for the staff. A total of 162 glass slides from 109 patients over a period of 5 weeks were included in this validation and were assessed by all pathologists in both analyses (digital and conventional) to allow intraobserver comparison. The intraobserver agreement between the digital method (DM) and conventional method (CM) was considered almost perfect (κ ranged from 0.85 to 0.98, with 95% CI, ranging from 0.81 to 1). The most significant and frequent disagreements within trainees encompassed epithelial dysplasia grading and differentiation among severe dysplasia (carcinoma in situ) and oral squamous cell carcinoma. The most frequent pitfall from DM was lag in screen mirroring. The lack of details of inflammatory cells and the need for a higher magnification to assess dysplasia were pointed in one case each. The COVID-19 crisis has accelerated and consolidated the use of online meeting tools, which would be a valuable resource even in the post-pandemic scenario. Adaptation in laboratory workflow, the advent of digital pathology and remote reporting can mitigate the impact of similar future disruptions to the oral and maxillofacial pathology laboratory workflow avoiding delays in diagnosis and report, to facilitate timely management of head and neck cancer patients. Graphical abstract.

Cost-effectiveness of photobiomodulation therapy for the prevention and management of cancer treatment toxicities: a systematic review.

PURPOSE: To identify and summarize the evidence on the cost-effectiveness of photobiomodulation (PBM) therapy for the prevention and treatment of cancer treatment-related toxicities. METHODS: This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) and Meta-analysis Of Observational Studies in Epidemiology (MOOSE). Scopus, MEDLINE/PubMed, and Embase were searched electronically. RESULTS: A total of 1490 studies were identified, and after a two-step review, 4 articles met the inclusion criteria. The included studies analyzed the cost-effectiveness of PBM therapy used in the context of lymphedema for breast cancer and oral mucositis (OM) induced by chemotherapy and radiotherapy. Better outcomes were associated with PBM therapy. The incremental cost-effectiveness ratio ranged from 3050.75 USD to 5592.10 USD per grade 3-4 OM case prevented. PBM therapy cost 21.47 USD per percentage point reduction in lymphedema in comparison with 80.51 USD for manual lymph drainage and physical therapy. CONCLUSION: There is limited evidence that PBM therapy is cost-effective in the prevention and treatment of specific cancer treatment-related toxicities, namely, OM and breast cancer-related lymphedema. Studies may have underreported the benefits due to a lack of a comprehensive cost evaluation. This suggests a wider acceptance of PBM therapy at cancer treatment centers, which has thus far been limited by the number of robust clinical studies that demonstrate cost-effectiveness for the prevention and treatment of toxicities.

Oral leukoplakia and erythroplakia in young patients: a systematic review.

OBJECTIVE: The aim of this study was to estimate the frequency of oral leukoplakia and oral erythroplakia in young patients. STUDY DESIGN: The systematic review was based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and performed in the following electronic databases: PubMed, Scopus, and Embase. The studies included were cross-sectional, cohort, and diagnostic, which provided with clinical and microscopic data of patients younger than 40 years. The Critical Appraisal Checklist for Prevalence Studies from the Joanna Briggs Institute and the Quality Assessment Tool for Diagnostic Accuracy Studies were used to assess the risk of bias. RESULTS: Five studies met eligibility criteria and were included. The total number of patients from the studies was 1246, of which 115 were young patients (9.2%) with oral leukoplakia as the only oral potentially malignant disorder reported. Oral epithelial dysplasia was identified in 40 cases (34.7%), of which 8 (6.9%) presented malignant transformation. CONCLUSIONS: The frequency of oral leukoplakia is low in young patients. Observational studies are necessary for understanding oral leukoplakia and other oral potentially malignant disorders in younger patients.

Global prevalence of human papillomavirus-driven oropharyngeal squamous cell carcinoma following the ASCO guidelines: A systematic review and meta-analysis.

OBJECTIVES: to provide accurate information about the global prevalence of human papillomavirus (HPV) in oropharyngeal squamous cell carcinomas (OPSCC). MATERIAL AND METHODS: a systematic review was performed using three main electronic databases. Studies were independently assessed by two reviewers based on established eligibility criteria, to identify the prevalence of HPV-driven OPSCC following criteria defined by the American Society of Clinical Oncology. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Statistical software MedCalc was used to perform meta-analyses. RESULTS: from 2215 records found, 15 were included, reporting data from 6009 patients (time period range: 1980-2016), distributed in 11 countries. Eleven studies were considered as presenting low risk, and four as moderate risk of bias. Using proportion meta-analysis, pooled prevalence of HPV-driven OPSCC was 44.8 % (95 %CI: 36.4-53.5 %; i2 = 97.6 %), with the highest rates in New Zealand (74.5 %; 95 %CI: 60.9-85.3 %), and the lowest in Brazil (11.1 %; 95 %CI: 4.5-21.5 %). HPV prevalence was similar between males (45.7 %; 95 %CI: 36.5-55.0 %; i2 = 96.4 %) and females (42.2 %; 95 %CI: 34.3-50.5 %; i2 = 85.4 %). Mean/median age ranged from 59.1-67.1 years in the HPV-negative group, and from 55.7-63.5 years in the HPV-positive group. There was an overall discordance between testing by p16 (49.4 %; 95 %CI, 38.2-60.5 %; i2 = 96.2 %) and p16+ISH/PCR (44.7 %; 95 %CI, 33.5-56.2 %; i2 = 96.4 %). CONCLUSION: Overall pooled prevalence of HPV-driven OPSCC was approximately 45 %, with similar distribution among males and females. Double p16/HPV-DNA/RNA testing may be considered to increase specificity and prognostic accuracy.

The use of digital microscopy as a teaching method for human pathology: a systematic review.

Since digital microscopy (DM) has become a useful alternative to conventional light microscopy (CLM), several approaches have been used to evaluate students' performance and perception. This systematic review aimed to integrate data regarding the use of DM for education in human pathology, determining whether this technology can be an adequate learning tool, and an appropriate method to evaluate students' performance. Following a specific search strategy and eligibility criteria, three electronic databases were searched and several articles were screened. Eight studies involving medical and dental students were included. The test of performance comprised diagnostic and microscopic description, clinical features, differential, and final diagnoses of the specimens. The students' achievements were equivalent, similar or higher using DM in comparison with CLM in four studies. All publications employed question surveys to assess the students' perceptions, especially regarding the easiness of equipment use, quality of images, and preference for one method. Seven studies (87.5%) indicated the students' support of DM as an appropriate method for learning. The quality assessment categorized most studies as having a low bias risk (75%). This study presents the efficacy of DM for human pathology education, although the high heterogeneity of the included articles did not permit outlining a specific method of performance evaluation.