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. In engineered, multi-compartment tumouroids spatial transcriptomics revealed an upregulation of oncogenes in the ameloblastoma transcriptome where osteoblasts were present in the stromal compartment (bone stroma). Where a gingival fibroblast stroma was engineered, the ameloblastoma tumour transcriptome revealed increased matrix remodelling genes. This study provides evidence to show the stromal-specific effect on tumour behaviour and illustrates the importance of engineering biologically relevant stroma for engineered tumour models. Our novel results show that an engineered fibroblast stroma causes the upregulation of matrix remodelling genes in ameloblastoma which directly correlates to measured invasion in the model. In contrast the presence of a bone stroma increases the expression of oncogenes by ameloblastoma cells.

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset.

We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzhen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon; we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists. We show that some of the participants were capable to achieve pathologist-level performance at lymphocyte assessment. After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform.

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. There are a total of 28 features of oral epithelial dysplasia listed in the 5th edition of World Health Organization classification of tumours of the head and neck. Each of these features is poorly defined and subjective in its interpretation. Moreover, how these features contribute to dysplasia grading and risk stratification is even less well defined. This article discusses each of the features of oral epithelial dysplasia with examples and provides an overview of the common mimics, including the normal histological features of the oral mucosa which may mimic atypia. This article also highlights the paucity of evidence defining these features while offering suggested definitions. Ideally, these definitions will be refined, and the most important features identified to simplify the diagnosis of oral epithelial dysplasia. Digital whole slide images of the figures in this paper can be found at: https://www.pathogenesis.co.uk/r/demystifying-dysplasia-histology-dataset.

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. Specifically, the diagnostic and prognostic importance of mitotic number, mitotic type and intra-epithelial location is evaluated. Whole slide images (WSI) of OED (n = 60) and non-dysplastic tissue (n = 8) were prepared for analysis. Five-year follow-up data was collected. The total number of mitosis (TNOM), mitosis type and intra-epithelial location was manually evaluated on H&E images and a digital mitotic count performed on PHH3-stained WSI. Statistical associations between these features and OED grade, malignant transformation and OED recurrence were determined. Mitosis count increased with grade severity (H&E: p < 0.005; IHC: p < 0.05), and grade-based differences were seen for mitosis type and location (p < 0.05). The ratio of normal-to-abnormal mitoses was higher in OED (1.61) than control (1.25) and reduced with grade severity. TNOM, type and location were better predictors when combined with histological grading, with the most prognostic models demonstrating an AUROC of 0.81 for transformation and 0.78 for recurrence, exceeding conventional grading. Mitosis quantification and PHH3 staining can be an adjunct to conventional H&E assessment and grading for the prediction of OED prognosis. Validation on larger multicentre cohorts is needed to establish these findings.

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.

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).

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. We show that our Digital-HPV score generated from hematoxylin and eosin slides produces statistically significant patient stratifications in terms of overall and disease-specific survival. In addition, quantitative profiling of the spatial tumor microenvironment and analysis of the immune profiles show relatively high levels of lymphocytic infiltration in tumor and tumor-associated stroma. High levels of B cells and T cells and low macrophage levels were also identified in HPV-positive patients compared to HPV-negative patients, confirming different immune response patterns elicited by HPV infection in patients with HNSCC.

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. In this work, we propose a deep learning approach for the development of prognostic models for malignant transformation and their association with clinical outcomes in histology whole slide images (WSIs) of OED tissue sections. We train a weakly supervised method on OED cases (n = 137) with malignant transformation (n = 50) and mean malignant transformation time of 6.51 years (±5.35 SD). Stratified five-fold cross-validation achieved an average area under the receiver-operator characteristic curve (AUROC) of 0.78 for predicting malignant transformation in OED. Hotspot analysis revealed various features of nuclei in the epithelium and peri-epithelial tissue to be significant prognostic factors for malignant transformation, including the count of peri-epithelial lymphocytes (PELs) (p < 0.05), epithelial layer nuclei count (NC) (p < 0.05), and basal layer NC (p < 0.05). Progression-free survival (PFS) using the epithelial layer NC (p < 0.05, C-index = 0.73), basal layer NC (p < 0.05, C-index = 0.70), and PELs count (p < 0.05, C-index = 0.73) all showed association of these features with a high risk of malignant transformation in our univariate analysis. Our work shows the application of deep learning for the prognostication and prediction of PFS of OED for the first time and offers potential to aid patient management. Further evaluation and testing on multi-centre data is required for validation and translation to clinical practice. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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.

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. SPSS Statistics for Windows was used for a quantitative analysis of the data, with a descriptive analysis of the clinicopathological characteristics and the association between clinical variables and diagnoses. RESULTS: A total of 203 cases of epithelial SGT were included. Females were slightly more commonly (56.5%), with a mean age of 14.1 years. The palate was the most common site (43.5%), followed by the parotid gland (29%), lip (10%), and submandibular gland (7.5%). The predominant clinical presentation was a flesh-colored, smooth, and painless nodule. Pleomorphic adenoma (PA) was the most frequently diagnosed SGT (58.6%), followed by mucoepidermoid carcinoma (MEC) (26.6%). Surgery (90.8%) was the favored treatment option. CONCLUSIONS: Benign SGT in pediatric patients are more commonly benign than malignant tumors. Clinicians should keep PA and MEC in mind when assessing nodular lesions of possible salivary gland origin in pediatric patients.

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.

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.

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. The molecular mechanisms for ameloblastoma progression are poorly understood. A spatial model recapitulating the tumour and stroma was engineered to show that without a relevant stromal population, tumour invasion is quantitatively decreased. Where a relevant stroma was engineered in dense collagen populated by gingival fibroblasts, enhanced receptor activator of nuclear factor kappa-B ligand (RANKL) expression was observed and histopathological properties, including ameloblastoma tumour islands, developed and were quantified. Using human osteoblasts (bone stroma) further enhanced the biomimicry of ameloblastoma histopathological phenotypes. This work demonstrates the importance of the two key stromal populations, osteoblasts, and gingival fibroblasts, for accurate 3D biomimetic ameloblastoma modelling.

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 4.53 times higher chance of recurrence after treatment. We also found 6.52 higher chances of MT of non-homogeneous OL. Another clinical feature related to higher MT chances is the location (floor of the mouth and tongue has 4.48 higher chances) and the size (OL with >200 mm2 in size has 4.10 higher chances of MT). Regarding habits, nonsmoking patients with OL have a 3.20 higher chance of MT. The only clinical feature related to higher chances of MT in patients with PVL was sex (females have a 2.50 higher chance of MT). CONCLUSIONS: Our study showed that some clinical features may indicate greater chances of recurrence after treatment and MT of OPMD.

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. METHODS: KAs were retrospectively reviewed over a 20-year period from three Oral and Maxillofacial Pathology Laboratories. Included cases were examined and the diagnosis confirmed under conventional microscopy. Immunohistochemistry with the use of a monoclonal antibody against calretinin was performed on included cases. The clinical, radiologic and histologic features of the seven new cases of KA were analysed and compared to existing cases in the literature. RESULTS: KAs presented at a mean age of 40 years with a nearly equal gender distribution and a mandibular predilection (65%). The majority (92%) of cases presented with localised swelling with associated pain in 32% of cases. Mixed density or internal calcifications were noted in 40% of cases. All tumours presented with bony expansion, with cortical destruction noted in 62% of cases. Histologically, all tumours consisted of solid and cystic follicles with surface parakeratinisation and lamellated accumulations of central keratin. In areas the cystic follicles had an epithelial lining suggestive of an OKC. There were focal luminal areas of loosely arranged polygonal cells reminiscent of the stellate reticulum. The basal cells consisted of columnar cells with evidence of palisading and prominent subnuclear vacuolisation. Of the cases treated via tumour resection, 27% presented with tumour recurrence. CONCLUSION: This case series reports seven additional cases of KA, taking the total to 26 reported cases. The identification of subtle histologic features, including focal stellate reticulum-like central areas, subnuclear vacuolisation and lamellated-type central keratinisation, are key in diagnosing KA. The radiologic features will often indicate signs of aggressiveness such as cortical destruction, differentiating KA from OKC. All cases were completely negative for calretinin IHC, limiting its use in distinguishing KA from OKC. Further large series are needed to expand the current understanding of this rare variant of ameloblastoma.

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.

Distribution and Frequency of Salivary Gland Tumours: An International Multicenter Study.

BACKGROUND: Salivary gland tumours (SGT) are a relatively rare group of neoplasms with a wide range of histopathological appearance and clinical features. To date, most of the epidemiological studies on salivary gland tumours are limited for a variety of reason including being out of date, extrapolated from either a single centre or country studies, or investigating either major or minor glands only. METHODS: This study aimed to mitigate these shortcomings by analysing epidemiological data including demographic, anatomical location and histological diagnoses of SGT from multiple centres across the world. The analysed data included age, gender, location and histological diagnosis from fifteen centres covering the majority of the world health organisation (WHO) geographical regions between 2006 and 2019. RESULTS: A total of 5739 cases were analysed including 65% benign and 35% malignant tumours. A slight female predilection (54%) and peak incidence between the fourth and seventh decade for both benign and malignant tumours was observed. The majority (68%) of the SGT presented in major and 32% in the minor glands. The parotid gland was the most common location (70%) for benign and minor glands (47%) for malignant tumours. Pleomorphic adenoma (70%), and Warthin's tumour (17%), were the most common benign tumours whereas mucoepidermoid carcinoma (26%) and adenoid cystic carcinoma (17%) were the most frequent malignant tumours. CONCLUSIONS: This multicentre investigation presents the largest cohort study to date analysing salivary gland tumour data from tertiary centres scattered across the globe. These findings should serve as a baseline for future studies evaluating the epidemiological landscape of these tumours.

Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models.

Oral epithelial dysplasia (OED) is a precursor state usually preceding oral squamous cell carcinoma (OSCC). Histological grading is the current gold standard for OED prognostication but is subjective and variable with unreliable outcome prediction. We explore if individual OED histological features can be used to develop and evaluate prognostic models for malignant transformation and recurrence prediction. Digitised tissue slides for a cohort of 109 OED cases were reviewed by three expert pathologists, where the prevalence and agreement of architectural and cytological histological features was assessed and association with clinical outcomes analysed using Cox proportional hazards regression and Kaplan-Meier curves. Within the cohort, the most prevalent features were basal cell hyperplasia (72%) and irregular surface keratin (60%), and least common were verrucous surface (26%), loss of epithelial cohesion (30%), lymphocytic band and dyskeratosis (34%). Several features were significant for transformation (p < 0.036) and recurrence (p < 0.015) including bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses and nuclear pleomorphism. This led us to propose two prognostic scoring systems including a '6-point model' using the six features showing a greater statistical association with transformation and recurrence (bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses, nuclear pleomorphism) and a 'two-point model' using the two features with highest inter-pathologist agreement (loss of epithelial cohesion and bulbous rete pegs). Both the 'six point' and 'two point' models showed good predictive ability (AUROC ≥ 0.774 for transformation and 0.726 for recurrence) with further improvement when age, gender and histological grade were added. These results demonstrate a correlation between individual OED histological features and prognosis for the first time. The proposed models have the potential to simplify OED grading and aid patient management. Validation on larger multicentre cohorts with prospective analysis is needed to establish their usefulness in clinical practice.

Oral Squamous Cell Carcinoma Frequency in Young Patients from Referral Centers Around the World.

Oral squamous cell carcinoma (OSCC) commonly affects older patients; however, several studies have documented an increase in its incidence among younger patients. Therefore, it is important to investigate if this trend is also found in different geographic regions. The pathology files of diagnostic and therapeutic institutions from different parts of the globe were searched for OSCC cases diagnosed from 1998 to 2018. Data regarding the sex, age, and tumor location of all cases, as well as the histologic grade and history of exposure to risk habits of cases diagnosed as OSCC in young patients (≤ 40 years of age) were obtained. The Chi-square test was used to determine any increasing trend. A total of 10,727 OSCC cases were identified, of which 626 cases affected young patients (5.8%). Manipal institution (India) showed the highest number of young patients (13.2%). Males were the most affected in both age groups, with the tongue and floor of the mouth being the most affected subsites. OSCC in young individuals were usually graded as well or moderately differentiated. Only 0.9% of the cases occurred in young patients without a reported risk habit. There was no increasing trend in the institutions and the period investigated (p > 0.05), but a decreasing trend was observed in Hong Kong and the sample as a whole (p < 0.001). In conclusion there was no increase of OSCC in young patients in the institutions investigated and young white females not exposed to any known risk factor represented a rare group of patients affected by OSCC.