A head-to-head comparison of four grading systems for oral epithelial dysplasia.

Aims: Oral epithelial dysplasia (OED) carries a risk of malignant transformation to oral squamous cell carcinoma. Clinical risk stratification for these patients is challenging, and reliant upon histological grading. The World Health Organisation (WHO) grading system is the current gold standard, although the binary system, two- and six-point prognostic models have also been proposed.

Pleomorphic adenoma and carcinoma ex-pleomorphic adenoma tumorigenesis: A proteomic analysis.

Objectives: To conduct a comprehensive proteomic analysis of normal salivary gland tissue, pleomorphic adenoma (PA), and carcinoma ex-pleomorphic adenoma (CXPA), and validate the proteomic findings using immunohistochemistry.

Methods: Six normal salivary gland tissues, seven PA and seven CXPA samples underwent laser microdissection followed by liquid chromatography coupled to mass spectrometry. Protein identification and quantification were performed using MaxQuant software.

Understanding interobserver variability of pathologists to improve oral epithelial dysplasia grading.

Objective: This study aimed to understand reasons for interobserver variability in the grading of oral epithelial dysplasia (OED) through a survey of pathologists to provide insight for improvements in the reliability and reproducibility of OED diagnoses.

Methods: The study design included quantitative and qualitative methodology. A pre-validated 31-item questionnaire was distributed to general, head and neck, and oral and maxillofacial histopathology specialists worldwide.

The Value of Labial Gland Biopsies as a Diagnostic Test for Sjögren's Syndrome.

Purpose: There are a number of diagnostic criteria that can be used to support a diagnosis of Sjögren's syndrome (SS), a chronic autoimmune condition often characterised by xerostomia and xerophthalmia. Of the available investigations, the most invasive is the labial gland biopsy (LGB) for histopathology, which is associated with a risk of long-term altered sensation to the lip. A positive histological diagnosis is currently considered to be one of the most objective criteria, however there is debate about the interobserver agreement between pathologists, as well as the sensitivity and specificity of this test.

A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia.

Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs).

ChatGPT and dentistry: a step toward the future.

This article aims to explore the integration of ChatGPT, an advanced conversational artificial intelligence model, in the field of dentistry. The review primarily consists of information related to the capabilities and functionalities of ChatGPT and how these abilities can aid dental professionals. This study includes data from research papers, case studies, and relevant literature on language models, as well as papers on dentistry, patient communication, dental education, and clinical decision-making.

Spatial transcriptomic interrogation of the tumour-stroma boundary in a 3D engineered model of ameloblastoma.

Stromal cells are key components of the tumour microenvironment (TME) and their incorporation into 3D engineered tumour-stroma models is essential for tumour mimicry. By engineering tumouroids with distinct tumour and stromal compartments, it has been possible to identify how gene expression of tumour cells is altered and influenced by the presence of different stromal cells. Ameloblastoma is a benign epithelial tumour of the jawbone.

Demystifying oral epithelial dysplasia: a histological guide.

Oral epithelial dysplasia is a histologically diagnosed potentially premalignant disorder of the oral mucosa, which carries a risk of malignant transformation to squamous cell carcinoma. The diagnosis and grading of oral epithelial dysplasia is challenging, with cases often referred to specialist oral and maxillofacial pathology centres for second opinion. Even still there is poor inter-examiner and intra-examiner agreement in a diagnosis.

Prognostic importance of mitosis quantification and PHH3 expression in oral epithelial dysplasia.

Oral epithelial dysplasia (OED) is diagnosed and graded using a range of histological features, making grading subjective and challenging. Mitotic counting and phosphohistone-H3 (PHH3) staining have been used for the prognostication of various malignancies; however, their importance in OED remains unexplored. This study conducts a quantitative analysis of mitotic activity in OED using both haematoxylin and eosin (H&E)-stained slides and immunohistochemical (IHC) staining for PHH3.

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.

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.

A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images.

The etiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens, such as alcohol, tobacco, and infection with human papillomavirus (HPV). Because HPV infection influences the prognosis, treatment, and survival of patients with HNSCC, it is important to determine the HPV status of these tumors. In this article, we propose a novel deep learning pipeline for HPV infection status prediction with state-of-the-art performance in HPV detection using only whole-slide images of routine hematoxylin and eosin-stained HNSCC sections.

A digital score of peri-epithelial lymphocytic activity predicts malignant transformation in oral epithelial dysplasia.

Oral squamous cell carcinoma (OSCC) is amongst the most common cancers, with more than 377,000 new cases worldwide each year. OSCC prognosis remains poor, related to cancer presentation at a late stage, indicating the need for early detection to improve patient prognosis. OSCC is often preceded by a premalignant state known as oral epithelial dysplasia (OED), which is diagnosed and graded using subjective histological criteria leading to variability and prognostic unreliability.

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.

Epithelial salivary gland tumors in pediatric patients: An international collaborative study.

Objective: Salivary gland tumors (SGT) are a diverse group of uncommon neoplasms that are rare in pediatric patients. This study aimed to characterize the clinicopathological profile of pediatric patients affected by SGT from a large case series derived from an international group of academic centers.

Study Design: A retrospective analysis of pediatric patients with SGT (0-19 years old) diagnosed between 2000 and 2021 from Brazil, South Africa, and the United Kingdom was performed.

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.

Modelling stromal compartments to recapitulate the ameloblastoma tumour microenvironment.

Tumour development and progression is dependent upon tumour cell interaction with the tissue stroma. Bioengineering the tumour-stroma microenvironment (TME) into 3D biomimetic models is crucial to gain insight into tumour cell development and progression pathways and identify therapeutic targets. Ameloblastoma is a benign but locally aggressive epithelial odontogenic neoplasm that mainly occurs in the jawbone and can cause significant morbidity and sometimes death.

Clinical predictors of malignant transformation and recurrence in oral potentially malignant disorders: A systematic review and meta-analysis.

Objective: We performed a systematic review dedicated to pooling evidence for the associations of clinical features with malignant transformation (MT) and recurrence of 3 oral potentially malignant disorders (OPMDs) (actinic cheilitis [AC], oral leukoplakia [OL], and proliferative verrucous leukoplakia [PVL]).

Study Design: We selected studies that included clinical features and risk factors (age, sex, site, size, appearance, alcohol intake, tobacco use, and sun exposure) of OL, PVL, and AC associated with recurrence and/or MT.

Results: Based on the meta-analysis results, non-homogeneous OL appears to have a 4.

Keratoameloblastoma: A Report of Seven New Cases and Review of Literature.

Background: Keratoameloblastoma (KA) is an uncommon and controversial variant of ameloblastoma exhibiting central keratinisation. Due to their rarity, there is limited information in the literature on their clinical, radiologic and histologic features. This study adds seven additional cases of KA to the literature, and reviews the current published literature on this rare entity.

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.