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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]).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.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 with200 mmOur study showed that some clinical features may indicate greater chances of recurrence after treatment and MT of OPMD.

This systematic review aimed to identify the molecular alterations of head and neck rhabdomyosarcomas (HNRMS) and their prognostic values.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.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.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.

The rates of syphilis in the UK are rising. Syphilis is a bacterial sexually transmitted infection caused by Treponema pallidum. If left untreated, it can have serious health implications. Diagnosing syphilis remains a challenge due to its diverse spectrum of clinical presentations. Oral manifestations can present in the primary, secondary and tertiary stages of the disease. Therefore, dental professionals are well positioned to detect the signs. This article reviews the stages of the disease, its oral manifestations, and conditions that syphilis may mimic. A case report of a patient presenting in the primary stage of the disease is discussed. CPD/Clinical Relevance: Dental professionals should be able to identify the oral signs of syphilis because prompt referral in the early stages of disease can prevent long-term syphilitic complications.

Oral squamous cell carcinoma (OSCC) is amongst the most common cancers worldwide, 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 (n= 137) cases with transformation (n= 50) status and mean malignant transformation time of 6.51 years (±5.35 SD). Performing stratified 5-fold cross-validation achieves an average AUROC of ∼0.78 for predicting malignant transformations in OED. Hotspot analysis reveals various features from 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 using the Epithelial layer NC (p< 0.05, C-index = 0.73), Basal layer NC (p< 0.05, C-index = 0.70) and PEL count (p< 0.05, C-index = 0.73) shown association of these features with a high risk of malignant transformation. Our work shows the application of deep learning for prognostication and progression free survival (PFS) prediction of OED for the first time and has a significant potential to aid patient management. Further evaluation and testing on multi-centric data is required for validation and translation to clinical practice.

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.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.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.

Objective Intraoral, primary, CD30-positive (CD30+) T-cell lymphoproliferative disorders (TLPDs) are uncommon, and their clinicopathologic presentation and management can vary and may be challenging. Herein, we present a retrospective study of 4 examples of self-regressing primary CD30+ TLPD affecting the gingiva. Study Design Archived files were retrospectively reviewed for oral CD30+ TLPDs featuring (1) proper immunohistochemical documentation, (2) Epstein-Barr virus negativity, (3) adequate follow-up information corroborating regression, and (4) no history of hematopoietic malignancy or related-mucocutaneous disease. Results Three women and 1 man (age range, 55-82 years; mean, 68.3 years) presented with rapidly growing gingival ulcers. Microscopic evaluation revealed diffuse infiltration by sheets of large, atypical cells admixed with lymphocytes and eosinophils, showing angiocentric distribution, focal neurotropism, and muscle infiltration. The lesional cells consistently stained for CD3 and CD30 and were variably immunoreactive against CD2, CD4, CD5, CD7, and CD8, but were negative for ALK1 and EBV-encoded small RNA. TCR-γ gene rearrangement studies revealed a monoclonal T-cell population in 1 case. All lesions showed complete regression 2 to 8 weeks postoperatively (mean follow-up, 4.5 weeks). Conclusions Notwithstanding their alarming clinicopathologic appearance, there are CD30+ TLPDs confined to the oral cavity that have an indolent course. However, clinical staging is essential to exclude aggressive systemic malignancy.

Bioengineering facets of the tumour microenvironment (TME) are essential in 3D tissue models to accurately recapitulate tumour progression. Stromal cells are key components of the TME and their incorporation into 3D biomimetic bioengineered tumour-stroma models is essential to be able to mimic the TME. By engineering tumouroids with distinct tumour and stromal compartments, it has been possible to identify how gene expression is altered by the presence of different stromal cells using spatial transcriptomics. Ameloblastoma is a benign epithelial tumour of the jawbone and in engineered multi-compartment tumouroids increased expression of oncogenes was found where osteoblasts (bone stroma) were present. Engineering a gingival fibroblast stroma resulted in increased matrix remodelling genes in the ameloblastoma tumour. 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 an osteoblast/bone stroma increases the expression of oncogenes by ameloblastoma cells.

Epidemiological studies have shown that head and neck cancer (HNC) is a complex multistage process that in part involves exposure to a combination of carcinogens and the capacity of certain drug-metabolising enzymes including cytochrome P450 (CYP) to detoxify or activate such carcinogens. In this study, CYP1A1, CYP1B1 and CYP2W1 expression in HNC was correlated with potential as target for duocarmycin prodrug activation and selective therapy. In the HNC cell lines, elevated expression was shown at the gene level for CYP1A1 and CYP1B1 whereas CYP2W1 was hardly detected. However, CYP2W1 was expressed in FaDu and Detroit-562 xenografts and in a cohort of human HNC samples. Functional activity was measured in Fadu and Detroit-562 cells using P450-Glo™ assay. Antiproliferative results of duocarmycin prodrugs ICT2700 and ICT2706 revealed FaDu and Detroit-562 as the most sensitive HNC cell lines. Administration of ICT2700 in vivo using a single dose of ICT2700 (150 mg/kg) showed preferential inhibition of small tumour growth (mean size of 60 mm3) in mice bearing FaDu xenografts. Significantly, our findings suggest a potential targeted therapeutic approach to manage HNCs by exploiting intratumoural CYP expression for metabolic activation of duocarmycin-based prodrugs such as ICT2700.

Salivary gland tumors (SGT) are aheterogeneous neoplasms with large morphological diversity and overlapping features. Recently, numerous artificial intelligence (AI) methods shown for reproducible histological diagnosis and prognosis. However, their application to SGT has not been reported to date. This study aims to examine if AI can be used to differentiate between different SGT subtypes based on the analysis of digitized whole-slide images (WSIs) of Haematoxylin and Eosin (H&E) stained slides. A two-stage machine learning (ML) algorithm was developed and tested on 240 scanned H&E WSIs of SGT cases using an open-source bioimage analysis software (QuPath) to train and analyze features on representative regions of interest. The first classifier was designed to differentiate between two benign and four malignant SGT subtypes with an equal split between benign and malignant SGTs (n = 120 each), while the second classifier was used for malignant SGT subtyping (n = 120). Features extracted using the ML classifiers were also analysed using deep learning (DL) networks to determine any performance improvements. Our first classifier showed excellent accuracy for automated differentiation between benign and malignant SGTs (F1-score = 0.90). The second classifier also performed well for differentiation between four different malignant SGTs (average F1 = 0.92). Significant differences between cellularity, nuclear hematoxylin, cytoplasmic eosin, and nucleus/cell ratio (p 0.80), with EfficientNet-B0 giving the best performance (F1 = 0.87) but with inferior accuracy than the ML classifier for malignant subtyping (highest F1 = 0.60 for ResNet-18 and ResNet-50). Our novel findings show that AI can be used for automated differentiation between benign and malignant SGT and tumor subtyping on H&E images. Analysis of a larger multicentre cohort using ML and DL at the WSI level is required to establish the significance and clinical usefulness of these findings.

Background Burkitt lymphoma (BL) is an aggressive B-cell lymphoma with three variants (endemic, sporadic, and immunodeficiency-associated), presenting with specific epidemiological and clinical features. Burkitt lymphoma affects the head and neck region (BLHN) in approximately 10% of cases. The aim of this study was to undertake a comparative analysis of the clinicopathologic and immunohistochemical (IHC) features of BLHN diagnosed in patients from Africa, Guatemala, and Brazil. Methods Cases diagnosed as BLHN were collected from the files of six oral pathology laboratory services (Brazil, South Africa, and Guatemala), and one Brazilian pediatric oncology hospital from 1986 to 2020. Clinicopathological and IHC data, and Epstein-Barr virus (EBV) status by in situ hybridization data for each case were reviewed and described. Results Of the 52 cases, BLHN was predominant in pediatric patients [43 (82.69%)] and males [43 (82.69%)], with a mean age of 11.26 ± 9.68 years (range, 1-39 years). Neck and cervical lymph nodes [14 (26.92%)], and involvement of both maxilla and mandible [8 (15.38%)], were the most common anatomical sites. Clinically, tumor/swelling [40 (31.25%)], cervical lymphadenopathy [14 (10.94%)], pain [12 (9.38%)], and bone destruction [12 (9.38%)] were frequent findings. All cases showed typical morphological characteristics of BL. IHC profiles included positivity for CD20 [52 (100%)], CD10 [38 (79.17%)], Bcl6 [29 (87.88%)], and c-Myc protein [18 (81.82%)]. EBV was positive in 18 cases (62.07%). The Ki-67 index ranged from 90-100%. Conclusion The clinicopathological and EBV profile of BLHN in South African, Guatemalan and Brazilian patients is similar.

Objective The aim of this study was to investigate automated feature detection, segmentation, and quantification of common findings in periapical radiographs (PRs) by using deep learning (DL)–based computer vision techniques. Study Design Caries, alveolar bone recession, and interradicular radiolucencies were labeled on 206 digital PRs by 3 specialists (2 oral pathologists and 1 endodontist). The PRs were divided into “Training and Validation” and “Test” data sets consisting of 176 and 30 PRs, respectively. Multiple transformations of image data were used as input to deep neural networks during training. Outcomes of existing and purpose-built DL architectures were compared to identify the most suitable architecture for automated analysis. Results The U-Net architecture and its variant significantly outperformed Xnet and SegNet in all metrics. The overall best performing architecture on the validation data set was “U-Net+Densenet121” (mean intersection over union [mIoU] = 0.501; Dice coefficient = 0.569). Performance of all architectures degraded on the “Test” data set; “U-Net” delivered the best performance (mIoU = 0.402; Dice coefficient = 0.453). Interradicular radiolucencies were the most difficult to segment. Conclusions DL has potential for automated analysis of PRs but warrants further research. Among existing off-the-shelf architectures, U-Net and its variants delivered the best performance. Further performance gains can be obtained via purpose-built architectures and a larger multicentric cohort.