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. We aim to determine if the diagnostic value of the LGB is significant enough to warrant the surgical procedure and its associated risks. METHODS: This study involved assessing the degree of agreement between members of a pathology team for a cohort of 50 LGBs taken for the purpose of confirming or excluding SS. The Tarpley system was used, which involves the allocation of a 'focus score'. Additionally, the histological diagnoses were compared to the relevant serological findings where available. RESULTS: All cases within the cohort had adequate tissue for assessment. 84% agreement (Cohen's Kappa = 0.585) was seen between the current team's consensus and the original reporting pathologist on whether the appearance was supportive of SS. However, only 58% agreement was seen for focus scores (Weighted Kappa = 0.496). The agreement between the serology result and whether the histology was supportive of SS was 79% (Cohen's Kappa = 0.493). CONCLUSION: The findings raise the possibility that undue emphasis is placed on the value of a histological SS diagnosis. The current system for assessing and grading these biopsies is ambiguous in nature, with a low threshold considered indicative of SS. Due to the risk of complications associated with a LGB, alternative minimally invasive investigations should always be considered. The histological findings in isolation, particularly when a low focus score is seen, may not be predictive of a diagnosis of SS.

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). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p < 0.001). Nuclear analyses elucidated the presence of peri-epithelial and intra-epithelial lymphocytes in highly predictive patches of transforming cases (p < 0.001). This is the first study to propose a completely automated, explainable, and externally validated algorithm for predicting OED transformation. Our algorithm shows comparable-to-human-level performance, offering a promising solution to the challenges of grading OED in routine clinical practice.

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. A systematic approach was used to select relevant studies and literature. The selection criteria focused on papers that specifically discussed the integration of language models, ChatGPT in particular, in dentistry and their applications. The study findings revealed that ChatGPT has significant potential to revolutionize dentistry by offering various applications and benefits. It can enhance patient engagement and understanding through personalized oral health information and guidance. In dental education, ChatGPT can provide interactive learning, case studies, and virtual patient simulations. ChatGPT can also assist researchers in analyzing dental literature, identifying patterns, and generating insights. Moreover, it supports dentists with evidence-based recommendations, treatment options, and diagnostic support. Integrating ChatGPT in dentistry can be highly beneficial, but it is crucial to address ethical considerations, accuracy, and privacy concerns. Responsible implementation and continuous improvement of its functionalities are necessary to ensure that patient care and outcomes are improved.

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.

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.

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.

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.

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.