CSChighE-cadherinlow immunohistochemistry panel predicts poor prognosis in oral squamous cell carcinoma.

Identifying marker combinations for robust prognostic validation in primary tumour compartments remains challenging. We aimed to assess the prognostic significance of CSC markers (ALDH1, CD44, p75NTR, BMI-1) and E-cadherin biomarkers in OSCC. We analysed 94 primary OSCC and 67 metastatic lymph node samples, including central and invasive tumour fronts (ITF), along with clinicopathological data. We observed an increase in ALDH1+/CD44+/BMI-1- tumour cells in metastatic lesions compared to primary tumours. Multivariate analysis highlighted that elevated p75NTR levels (at ITF) and reduced E-cadherin expression (at the tumour centre) independently predicted metastasis, whilst ALDH1high exhibited independent predictive lower survival at the ITF, surpassing the efficacy of traditional tumour staging. Then, specifically at the ITF, profiles characterized by CSChighE-cadherinlow (ALDH1highp75NTRhighE-cadherinlow) and CSCintermediateE-cadherinlow (ALDH1 or p75NTRhighE-cadherinlow) were significantly associated with worsened overall survival and increased likelihood of metastasis in OSCC patients. In summary, our study revealed diverse tumour cell profiles in OSCC tissues, with varying CSC and E-cadherin marker patterns across primary tumours and metastatic sites. Given the pivotal role of reduced survival rates as an indicator of unfavourable prognosis, the immunohistochemistry profile identified as CSChighE-cadherinlow at the ITF of primary tumours, emerges as a preferred prognostic marker closely linked to adverse outcomes in OSCC.

[Precancers of the oral mucosa: clinic, diagnostics]. / Predraki slizistoi obolochki rta: klinika i diagnostika.

OBJECTIVE: The aim of the study. Improving the efficiency of diagnosis and detailing the features of the clinic of «potentially malignant¼ diseases of the oral mucosa. MATERIALS AND METHODS: Clinical and laboratory examination of 124 patients of the department of oral mucosa diseases aged 35 to 80 years, among whom there were 75 women and 49 men, with diseases such as erythroplakia - 12 patients, verrucous leukoplakia - 52 patients, erosive form of leukoplakia - 35 patients, cheilitis Manganotti - 25 patients. Histological and immunohistochemical methods of investigation were used as diagnostics. To assess the proliferative activity of epithelial cells, the determination of the Ki-67 index was used. The synthesis of keratin 15 (K15) in epithelial layers was determined as a diagnostic criterion for the severity of neoplasia. The expression of human papillomavirus type 16 (HPV 16) antigens and p16INK4a protein in epithelial cells was studied, as well as the expression of p53 protein. RESULTS: A high prevalence of p53 mutations was observed in patients with erythroplakia. In leukoplakia, the expression of the Ki-67 protein was detected in the cell nuclei in both the basal and parabasal layers of the multilayer squamous epithelium, in 77% of cases, the expression of the p16INK4a protein in the epithelial nuclei with varying degrees of dysplastic changes was noted, and a positive reaction to HPV16 was also observed in the cell nuclei and cytoplasm of epithelial cells in the basal, parabasal and spiny epithelial layers. The appearance of K15 in the cytoplasm of cells above the basal layer with abrasive precancerous cheilitis was found in 48% of cases. CONCLUSION: To diagnose early manifestations of neoplastic processes in «potentially malignant¼ diseases of the oral mucosa, it is necessary to use both classical histological and immunohistochemical methods of investigation with various markers.

The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study.

BACKGROUND: Machine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems in the field of potentially malignant lesions, oral cancer, periodontal diseases, salivary gland disease, oral infections, immune-mediated disease, and others. AI can detect micro-features beyond human eyes and provide solution in critical diagnostic cases. OBJECTIVE: The objective of this study was developing a software with all needed feeding data to act as AI-based program to diagnose oral diseases. So our research question was: Can we develop a Computer-Aided Software for accurate diagnosis of oral diseases based on clinical and histopathological data inputs? METHOD: The study sample included clinical images, patient symptoms, radiographic images, histopathological images and texts for the oral diseases of interest in the current study (premalignant lesions, oral cancer, salivary gland neoplasms, immune mediated oral mucosal lesions, oral reactive lesions) total oral diseases enrolled in this study was 28 diseases retrieved from the archives of oral maxillofacial pathology department. Total 11,200 texts and 3000 images (2800 images were used for training data to the program and 100 images were used as test data to the program and 100 cases for calculating accuracy, sensitivity& specificity). RESULTS: The correct diagnosis rates for group 1 (software users), group 2 (microscopic users) and group 3 (hybrid) were 87%, 90.6, 95% respectively. The reliability for inter-observer value was done by calculating Cronbach's alpha and interclass correlation coefficient. The test revealed for group 1, 2 and 3 the following values respectively 0.934, 0.712 & 0.703. All groups showed acceptable reliability especially for Diagnosis Oral Diseases Software (DODS) that revealed higher reliability value than other groups. However, The accuracy, sensitivity & specificity of this software was lower than those of oral pathologists (master's degree). CONCLUSION: The correct diagnosis rate of DODS was comparable to oral pathologists using standard microscopic examination. The DODS program could be utilized as diagnostic guidance tool with high reliability & accuracy.

Use of digital strategies in the diagnosis of oral squamous cell carcinoma: a scoping review.

Telediagnosis uses information and communication technologies to support diagnosis, shortening geographical distances. It helps make decisions about various oral lesions. The objective of this scoping review was to map the existing literature on digital strategies to assist in the diagnosis of oral squamous cell carcinoma. this review was structured based on the 5-stage methodology proposed by Arksey and O'Malley, the Joanna Briggs Institute Manual for Evidence Synthesis and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. The methods were registered on the Open Science Framework. The research question was: What digital strategies have been used to assist in the diagnosis of oral squamous cell carcinoma? The search was conducted on PubMed/MEDLINE, Scopus, Web of Science, Embase, and ScienceDirect. Inclusion criteria comprised studies on telediagnosis, teleconsultation or teleconsultation mediated by a professional and studies in English, without date restrictions. The search conducted in June 2023 yielded 1,798 articles, from which 16 studies were included. Telediagnosis was reported in nine studies, involving data screening through applications, clinical images from digital cameras, mobile phones or artificial intelligence. Histopathological images were reported in four studies. Both, telediagnosis and teleconsultation, were mentioned in seven studies, utilizing images and information submission services to platforms, WhatsApp or applications. One study presented teleconsultations involving slides and another study introduced teleconsultation mediated by a professional. Digital strategies telediagnosis and teleconsultations enable the histopathological diagnosis of oral cancer through clinical or histopathological images. The higher the observed diagnostic agreement, the better the performance of the strategy.

Metastatic sarcomas of the oral cavity: A systematic review.

Oral metastatic sarcomas (OMSs) occur only occasionally, and information about their characteristics is based on the restricted number of cases reported in the literature. This study aims to systematically review the English literature to recognize the clinicopathologic characteristics of OMSs. An electronic search was performed in PubMed Central and Scopus databases. The search included all the published articles (human case reports and case series) up till April 2023, with no time restrictions. OMSs were slightly more prevalent in males in their fifth to seventh decades of life. However, a high percentage of OMSs has been reported in the second decade of life. Lower extremities, breasts and uterus are the most common primary origin of metastatic sarcoma. Gingiva and mandible were common locations in the oral cavity for metastatic deposits. Generally, they demonstrated widespread affliction. The mean time interval between primary tumor detection and diagnosis of the oral metastasis was about 33.54 ± 36.19 months. Death was reported in 83 patients (67.48 %) with a mean survival rate of 7.98 ± 10.30 months. The most common microscopic tumor types were leiomyosarcoma (n = 21, 17 %), followed by angiosarcoma (n = 20, 16.26 %) and osteosarcoma (n = 18, 14.63 %). In conclusion, while oral metastases of sarcomas are not common, those should be considered in the differential diagnosis of the oral lesions. Although OMSs show a high occurrence in the 7th decade of the life, the average age of patients with oral involvement is lower than the overall metastatic lesions. OMSs may present as widespread disease with poor prognosis.

An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research.

INTRODUCTION: Oral epithelial dysplasia (OED) is a precancerous histopathological finding which is considered the most important prognostic indicator for determining the risk of malignant transformation into oral squamous cell carcinoma (OSCC). The gold standard for diagnosis and grading of OED is through histopathological examination, which is subject to inter- and intra-observer variability, impacting accurate diagnosis and prognosis. The aim of this review article is to examine the current advances in digital pathology for artificial intelligence (AI) applications used for OED diagnosis. MATERIALS AND METHODS: We included studies that used AI for diagnosis, grading, or prognosis of OED on histopathology images or intraoral clinical images. Studies utilizing imaging modalities other than routine light microscopy (e.g., scanning electron microscopy), or immunohistochemistry-stained histology slides, or immunofluorescence were excluded from the study. Studies not focusing on oral dysplasia grading and diagnosis, e.g., to discriminate OSCC from normal epithelial tissue were also excluded. RESULTS: A total of 24 studies were included in this review. Nineteen studies utilized deep learning (DL) convolutional neural networks for histopathological OED analysis, and 4 used machine learning (ML) models. Studies were summarized by AI method, main study outcomes, predictive value for malignant transformation, strengths, and limitations. CONCLUSION: ML/DL studies for OED grading and prediction of malignant transformation are emerging as promising adjunctive tools in the field of digital pathology. These adjunctive objective tools can ultimately aid the pathologist in more accurate diagnosis and prognosis prediction. However, further supportive studies that focus on generalization, explainable decisions, and prognosis prediction are needed.

Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture.

PROBLEM: Oral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment. AIM: This study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer. METHODS: A deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture. RESULTS: The improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field. CONCLUSION: Integrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies.

Rare presentation of solitary fibrous tumour in the floor of the mouth.

A male in his 20s, a tobacco chewer, presented to the outpatient department with a history of painless, slowly progressive swelling in the floor of the mouth. After a thorough history and clinical examination, MRI was done and the tumour was completely excised. Histopathological examination revealed the mass to be a solitary fibrous tumour, confirmed with immunohistochemical markers. On subsequent follow-ups, the patient was found to be asymptomatic with no clinical signs of recurrence.

Congenital orofacial teratoma in a 22-week foetus.

No abstract available.

Applying Machine Learning for Enhanced MicroRNA Analysis: A Companion Risk Tool for Oral Squamous Cell Carcinoma in Standard Care Incisional Biopsy.

Machine learning analyses within the realm of oral cancer outcomes are relatively underexplored compared to other cancer types. This study aimed to assess the performance of machine learning algorithms in identifying oral cancer patients, utilizing microRNA expression data. In this study, we implemented this approach using a panel of oral cancer-associated microRNAs sourced from standard incisional biopsy specimens to identify cases of oral squamous cell carcinomas (OSCC). For the model development process, we used a dataset comprising 30 OSCC and 30 histologically normal epithelium (HNE) cases. We initially trained a logistic regression prediction model using 70 percent of the dataset, while reserving the remaining 30 percent for testing. Subsequently, the model underwent hyperparameter tuning resulting in enhanced performance metrics. The hyperparameter-tuned model exhibited high accuracy (0.894) and ROC AUC (0.898) in predicting OSCC. Testing the model on cases of potentially malignant disorders (OPMDs) revealed that leukoplakia with mild dysplasia was predicted as having a high risk of progressing to OSCC, emphasizing machine learning's advantage over histopathology in detecting early molecular changes. These findings underscore the necessity for further refinement, incorporating a broader set of variables to enhance the model's predictive capabilities in assessing the risk of oral potentially malignant disorders.

Deep neural network uncertainty estimation for early oral cancer diagnosis.

BACKGROUND: Early diagnosis in oral cancer is essential to reduce both morbidity and mortality. This study explores the use of uncertainty estimation in deep learning for early oral cancer diagnosis. METHODS: We develop a Bayesian deep learning model termed 'Probabilistic HRNet', which utilizes the ensemble MC dropout method on HRNet. Additionally, two oral lesion datasets with distinct distributions are created. We conduct a retrospective study to assess the predictive performance and uncertainty of Probabilistic HRNet across these datasets. RESULTS: Probabilistic HRNet performs optimally on the In-domain test set, achieving an F1 score of 95.3% and an AUC of 96.9% by excluding the top 30% high-uncertainty samples. For evaluations on the Domain-shift test set, the results show an F1 score of 64.9% and an AUC of 80.3%. After excluding 30% of the high-uncertainty samples, these metrics improve to an F1 score of 74.4% and an AUC of 85.6%. CONCLUSION: Redirecting samples with high uncertainty to experts for subsequent diagnosis significantly decreases the rates of misdiagnosis, which highlights that uncertainty estimation is vital to ensure safe decision making for computer-aided early oral cancer diagnosis.

Oral Cavity Squamous Cell Carcinoma: Review of Pathology, Diagnosis, and Management.

Squamous cell carcinoma of the oral cavity presents a significant global health burden, primarily due to risk factors such as tobacco smoking, smokeless tobacco use, heavy alcohol consumption, and betel quid chewing. Common clinical manifestations of oral cavity cancer include visible lesions and sores, often accompanied by pain in advanced stages. Diagnosis relies on a comprehensive assessment involving detailed history, physical examination, and biopsy. Ancillary imaging studies and functional evaluations aid in accurate staging and facilitate treatment planning. Prognostic information is obtained from histopathological factors, such as tumor grade, depth of invasion, lymphovascular invasion, and perineural invasion. Notably, lymph node metastasis, found in approximately half of the patients, carries significant prognostic implications. Effective management necessitates a multidisciplinary approach to optimize patient outcomes. Surgical resection is the backbone of treatment, aimed at complete tumor removal while preserving functional outcomes. Adjuvant therapies, including radiation and chemotherapy, are tailored according to pathological factors. Further work in risk stratification and treatment is necessary to optimize outcomes in squamous cell carcinoma of the oral cavity.

Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection.

The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset-PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.

Update on Oral and Oropharyngeal Cancers: A guide for oral health care providers.

Oral squamous cell carcinomas (OSCC) signs and symptoms may be first identified by dental hygienists during routine extra and intra-oral examinations. A comprehensive extra-oral and intra-oral examination during regular dental hygiene assessment is paramount to identifying oral potentially malignant disorders (OPMD) and cancerous lesions for timely referral and treatment. Integrating a systematic list of questions during the medical and dental assessment along with careful visual and tactile examinations is critical to identifying OPMDs and cancerous lesions. Understanding the relationship between oropharyngeal squamous cell carcinomas (OPSCC) and Human Papilloma Virus (HPV) and how vaccination can prevent HPV-related OPSCC is critical to providing evidence-based recommendations and care. The purpose of this report is to provide an update on current epidemiological trends of OSCC and OPSCC rates in the United States (US) and provide the latest evidence on what dental hygienists must know to improve health outcomes and mitigate the consequences of undiagnosed cancer. This report considers enduring challenges with the annual rise in OPSCC rates and the public health burden of HPV-related cancers in the US. Emphasis on regular, quality continuing education about OSCC and OPSCC is emphasized along with recommendations for evidence-based training.

Shaping the future of oral cancer diagnosis: advances in salivary proteomics.

INTRODUCTION: Saliva has gained increasing attention in the quest for disease biomarkers. Because it is a biological fluid that can be collected is an easy, painless, and safe way, it has been increasingly studied for the identification of oral cancer biomarkers. This is particularly important because oral cancer is often diagnosed at late stages with a poor prognosis. AREAS COVERED: The review addresses the evolution of the experimental approaches used in salivary proteomics studies of oral cancer over the years and outlines advantages and pitfalls related to each one. In addition, examines the current landscape of oral cancer biomarker discovery and translation focusing on salivary proteomic studies. This discussion is based on an extensive literature search (PubMed, Scopus and Google Scholar). EXPERT OPINION: The introduction of mass spectrometry has revolutionized the study of salivary proteomics. In the future, the focus will be on refining existing methods and introducing powerful experimental techniques such as mass spectrometry with selected reaction monitoring, which, despite their effectiveness, are still underutilized due to their high cost. In addition, conducting studies with larger cohorts and establishing standardized protocols for salivary proteomics are key challenges that need to be addressed in the coming years.