Search Results (240)

Oral submucous fibrosis (OSMF) is a chronic, progressive fibrotic disorder with significant malignant potential, and limited effective treatments due to an incomplete understanding of its underlying molecular mechanisms. This narrative review synthesizes current evidence on the key molecular networks involved in OSMF pathogenesis, highlighting the central role of the TGF-β1/Smad, CTGF, and COL1A1 pathways in maintaining fibroblast activation, supporting myofibroblast activity, and promoting pathological collagen accumulation. It also integrates recent findings on how profibrotic signaling interacts with important angiogenic and epithelial growth factors such as VEGF, FGF, and EGF, creating a stage-dependent fibrotic microenvironment. In early OSMF, increased angiogenic signaling, especially through the VEGF/PI3K–Akt pathway, helps maintain blood supply. However, as the disease progresses, TGF-β-induced pathways become dominant, leading to vascular rarefaction, tissue hypoxia, epithelial instability and worsening fibrosis that increases the risk of malignant transformation. By critically appraising molecular, cellular, and translational studies, this review identifies key pathways linking fibrosis, angiogenesis, and epithelial changes in OSMF. It suggests that stage-specific and combined targeting of TGF-β1, CTGF, COL1A1, along with angiogenic and epithelial pathways, may help reverse fibrosis, restore normal tissue perfusion, reduce cancer risk, and support the development of better therapies and biomarkers beyond symptom relief. Full article

Objectives: Oral potentially malignant disorders (OPMDs) carry a variable risk of malignant transformation, making early detection important. Deep learning relies on specialized imaging, which is often inaccessible in routine practice, and detection from standard clinical photographs remains poorly characterized. We assessed the feasibility of automated OPMD detection from white-light intraoral photographs, compared two convolutional neural network paradigms (global classification with DenseNet-121 versus one-stage instance segmentation with YOLOv8), and identified the main barriers to clinical translation. Methods: A dataset of 1500 photographs (750 OPMD and 750 non-OPMD) from institutional archives and publicly accessible sources was split in an 80:20 ratio for training and testing. Lesion boundaries were annotated by three trainees using Cytomine and validated by three specialists. The DenseNet-121 and YOLOv8-large-segmentation models were evaluated for accuracy, sensitivity, specificity, precision, F1 score, and Wilson 95% CI. Results: DenseNet-121 required extensive manual lesion cropping to converge, negating the automation rationale. YOLOv8-large-segmentation reached 62.2% accuracy (95% CI 56.4 to 67.6), 75% sensitivity (95% CI 67.3 to 81.4), 59.7% precision (95% CI 52.4 to 66.5), 49.3% specificity (95% CI 41.3 to 57.4), and an F1 score of 66.5%, detecting lesions in 96% of the test set. High sensitivity was obtained at a low confidence threshold of 0.1, with correspondingly reduced specificity, and the model produced interpretable color-coded segmentation masks. Conclusions: One-stage instance segmentation is the more viable direction and yields spatially interpretable output, but performance at this dataset scale is not yet clinically sufficient. Dataset scale, threshold calibration, low specificity, and absent patient metadata are the key barriers to address. Full article

Background: Oral potentially malignant disorders (OPMDs) have a complex, but not consistently consistent, risk of transformation to oral squamous cell carcinoma (OSCC). The histopathologic grading of dysplasia is the traditional means of prognosis although there is considerable inter-observer variability and the tool has poor predictive value. Thus, image-based AI-based methods such as computational pathomics (CP), clinical-photograph deep learning (CDL), and optical or spectroscopic image analysis via machine learning (ML) or deep learning (DL) have been proposed as potential non-invasive risk stratification methods. Aim of the review is to identify the current state of the evidence for AI applications in the field of image-based diagnosis and treatment of OPMDs, their methodological characteristics and predictive accuracy, and priorities for future research. Methods: the scoping review was conducted using Joanna Briggs Institute methodology and PRISMA-ScR guidelines. The PubMed/MEDLINE, Scopus, Web of Science and Embase databases were searched between January 2018 and March 2026. Inclusion criteria were studies that used quantitative image analysis, ML or DL to diagnose, prognosticate, or stratify risk of OPMD in OPMD cohorts. A pre-piloted form was used to extract data which were then synthesized descriptively. Results: the 423 records identified included 24 (16 primary image-based studies and 8 contextual reviews) that met the inclusion criteria. The majority of studies were retrospective (15/16), and computational pathomics (n = 10), clinical-photograph deep learning and optical/spectroscopic imaging (n = 4) were emphasized. There was no study that used the conventional radiologic radiomics (CT, MRI, CBCT, or PET/CT) in OPMD cohorts. Overall, predictive performance was good, with AUROC values ranging from 0.73 to 0.96 for the detection of malignant transformation, 0.94 to 0.97 for OPMD versus OSCC discrimination, and 0.90 to 0.97 for dysplasia grading. Only 44% contained external validation, only 6% were prospective, and only limited adherence was made to IBSI, TRIPOD-AI and CLAIM standards. Conclusions: AI-based image recognition for OPMDs shows good predictive performance, and it has yet to be developed to a mature stage of the process. Future multicentre study, image standardisation, external validation and better reporting standards are needed. Remarkably, radiologic radiomics is an important and unexplored research gap in OPMD risk prediction. Full article

Background: Oral squamous cell carcinoma (OSCC) has increasingly been associated with oral microbiota and chronic periodontal inflammation. While major periodontal pathogens have been extensively studied, the role of Capnocytophaga gingivalis (C. gingivalis) in oral carcinogenesis remains unclear. This study investigated the prevalence and quantified the presence of C. gingivalis in benign oral lesions, oral potentially malignant disorders (OPMDs), and OSCCs, as well as its association with carcinogenesis-related gene expression. Methods: Ninety patients with periodontitis were included: 30 with benign lesions, 30 with OPMDs, and 30 with OSCCs. C. gingivalis quantification was performed using qPCR, while relative expression of VEGF, Cyclin D1, PIK3CA, DUSP16, mTOR, and MAPK14 was analyzed by RT-qPCR. Results: C. gingivalis was detected in 2 benign lesions, 11 OPMDs, and 18 OSCC samples (p < 0.001). Overall bacterial burden was significantly higher in OPMD and OSCC groups compared to benign lesions (p = 0.001). Expression of PIK3CA and MAPK14 was significantly increased in the OPMD and OSCC groups. In OSCC samples, C. gingivalis abundance positively correlated with VEGF and Cyclin D1 expression. Conclusions: C. gingivalis showed progressively increased prevalence and abundance across examined lesions and was associated with altered expression of genes involved in carcinogenesis, supporting its potential role in OSCC progression. Full article

Background/Objectives: Artificial intelligence (AI) has emerged as a transformative tool in oral medicine, where it holds significant promise for enhancing the diagnosis, treatment, and management of oral cancer. Our team aimed to develop a new tool, capable of diagnosing oral cancer utilizing the capabilities of AI. Methods: The task we aim to solve from computer vision’s perspective is an object detection task. Under this context, a detection is essentially a bounding box drawn around an oral lesion accompanied by the disease’s description. To solve the task at hand, we collected and annotated a wide set of images which were used for training our model. Specifically, we used 205 images of Oral Squamous Cell Carcinoma (OSCC). Following common practice, 80% of the total images were allocated for training, 10% for validation, and 10% for testing. The training set was used to optimize the model’s parameters across multiple iterations. The validation set served to prevent overfitting during training and to guide hyperparameter tuning. Lastly, the test set was used for evaluation of data that had not been previously seen by the model and had not influenced any decisions regarding its architecture or hyperparameters. Moreover, during evaluation, we supplemented the test set by adding 100 images of healthy mucosa to examine whether the model generated false positives on healthy tissue. To broaden the dataset’s coverage, we generated synthetic images by applying data augmentation techniques such as random rotation, scale and noise injection. The model’s architecture was based on YOLO11, which is a widely spread neural network architecture known for its balance between efficiency and performance, used in object detection tasks. Results: The model’s detections were accompanied by a confidence measure, which was used to filter out those with low confidence, and one could choose a lower threshold for maximizing precision or a higher threshold for maximizing recall. Among images that correspond to oral cancer (oral squamous cell carcinoma), the model achieved 59% precision and 41% recall on the validation set and 56% precision and 42% recall on the test set. The limitations of this study include the single institutional design and the relatively small sample size. The number of images used for model training was relatively small (205 images of oral squamous cell carcinoma), which may limit the generalizability of the findings. The main limitation is that the model distinguishes oral squamous cell carcinoma from healthy mucosa. In routine clinical practice, however, the diagnostic challenge is to differentiate oral cancer from a variety of benign and potentially malignant disorders that may present with similar clinical features. The inclusion of other oral lesions is planned in future studies. Conclusions: The efficiency of our AI application may be considered as encouraging, taking the pilot nature of the study into consideration. More clinical photos and better training of the model may lead to better precision and recall, enabling its inclusion in standard clinical practice. Larger multicenter datasets will be required for clinical implementation. AI-driven tools can assist in risk stratification, helping clinicians determine the best treatment plans by analyzing patient data and predicting the likelihood of recurrence or metastasis. AI’s potential extends beyond diagnosis and treatment; it also contributes to monitoring patient outcomes. As research and technology evolve, AI’s role in oral cancer is likely to become increasingly indispensable. Full article

Oral squamous cell carcinoma (OSCC) remains a major global health burden with limited improvement in survival rates. While traditional risk factors such as tobacco and alcohol are well established, increasing evidence highlights the role of the oral microbiome in carcinogenesis. Among microbial species, Candida albicans (C. albicans) has emerged as a potential contributor to tumor-promoting processes. Clinical studies consistently report increased fungal colonization in oral potentially malignant disorders and OSCC, with associations to disease severity and recurrence. Mechanistically, C. albicans contributes to carcinogenesis through acetaldehyde production, chronic inflammation, oxidative stress, epithelial signaling modulation, and extracellular vesicle (EV)-mediated communication. These pathways promote tumor microenvironment remodeling and epithelial transformation. However, conflicting evidence exists regarding causality, suggesting that fungal colonization may also result from tumor-associated ecological changes. From a translational perspective, C. albicans and EV-associated signatures may represent promising biomarkers and therapeutic targets, although further validation is required. This review highlights the emerging role of fungal–host interactions in OSCC and underscores their potential in microbiome-informed precision oncology. Full article

Oral squamous-cell carcinoma (OSCC) is a prevalent malignancy of the head and neck region. A delay in the diagnosis of OSCC often results in a high metastatic tendency, which is the main reason for the high patient mortality. Dynamic monitoring and management of the onset and progression of OSCC are critical for improving patient survival rates. Liquid biopsy technology—characterized by its non-invasive nature, procedural convenience, and capacity for longitudinal monitoring—is a promising adjunct to histopathological examination for the early diagnosis of OSCC. Epigenetic alterations, characterized by reversibility and long-term stability in physiological fluids, are critical enablers of liquid biopsy and its clinical utility. Advances in detection technologies, including quantitative polymerase chain reaction (qPCR), digital droplet PCR (ddPCR), next-generation sequencing (NGS), and electrochemical biosensors, have significantly facilitated the research and clinical translation of epigenetic biomarkers in oral liquid biopsies. However, translating epigenetic biomarkers from research discovery to clinical practice for OSCC remains hindered by several critical challenges: the scarcity of large-scale, rigorously designed cohort studies, limited multicenter validation, inconsistent preprocessing protocols, and a lack of harmonized analytical platforms. Finally, we propose a conceptual framework to outline potential clinical application models for these biomarkers. Full article

Oral squamous cell carcinoma (OSCC) frequently develops within chronically injured oral mucosa and may be preceded by clinically recognizable oral potentially malignant disorders (OPMDs), which provide an important window for cancer interception. This review examines how etiological exposures, persistent inflammation, and lesion-specific epithelial–stromal–immune interactions cooperate during the transition from mucosal injury to dysplasia, carcinoma in situ, and invasive OSCC. Major carcinogenic exposures, including tobacco, alcohol, and areca nut, are considered together with context-dependent contributors such as microbial dysbiosis, viral infection, and immune-mediated epithelial injury. At the molecular level, inflammation-driven oral carcinogenesis involves cytokine and chemokine amplification, oxidative and nitrosative stress, NF-κB and STAT3 activation, the COX-2/PGE₂ axis, genomic instability, field cancerization, epithelial–stromal crosstalk, angiogenesis, immune dysregulation, and epigenetic and non-coding RNA-mediated reprogramming. Emerging tools such as molecular risk assessment, liquid biopsy, optical imaging, spatially resolved profiling, and artificial intelligence-assisted models may improve identification of high-risk lesions, although most biomarkers require further prospective validation. Prevention should therefore integrate exposure control, biopsy-based diagnosis, local treatment when indicated, long-term surveillance, and trial-based precision strategies according to lesion risk, intervention window, and safety profile. This review supports a shift from lesion-centered management toward risk-adapted precision prevention in inflammation-driven oral carcinogenesis. Full article

Background/Objectives. Oral epithelial dysplasia exhibits unpredictable behavior, prompting research to identify biomarkers that may help predict its progression to malignancy. This study aimed to ascertain the prognostic value of biomarkers indicative of the epithelial–mesenchymal transition (EMT) process using immunohistochemistry. Methods. A systematic review was conducted using PubMed data from 1978 to June 2026. Articles that employed immunohistochemistry to identify cells exhibiting epithelial–mesenchymal transition changes in oral epithelial dysplasia were included. Exclusion criteria included in vivo studies, book chapters, reviews, conference abstracts, and studies lacking population descriptions. The risk of bias was assessed using the “JBI Checklist for Critical Appraisal of Case Series”. Results. A total of 21 articles were included, analyzing 57 biomarkers: 34 epithelial, 19 mesenchymal, two cell-proliferation biomarkers, and two tumor-suppressor biomarkers. The sample sizes varied significantly between the studies. Most articles employed semiquantitative assessment, cell percentage, and immunostaining intensity, with 12 demonstrating low risk of bias. Conclusions. Studies with conclusive results and a low risk of bias suggest that E-cadherin and Vimentin are valuable biomarkers for identifying early EMT in OED. However, the lack of statistical support means that this assertion should be viewed with caution. Full article

Background/Objectives: Oral potentially malignant disorders (OPMDs) require accurate risk stratification to identify patients at the highest risk for severe oral epithelial dysplasia (OED) or oral squamous cell carcinoma (OSCC). We developed and internally validated the oral cancer numerical index (OCNI), a quantitative risk score derived from clinical features and deep learning-based brush cytology measurements. Methods: This retrospective model development and internal validation study was conducted using data from the multicenter Grand Opportunity study. Prospectively recruited subjects with OPMD with complete data were divided at the subject level into a training set (n = 384) and a holdout test set (n = 164) using a 70:30 diagnosis-stratified split. The primary endpoint was severe OED or OSCC versus benign diagnoses, and mild and moderate OED. Predictors included age, sex, tobacco history, lesion color, lesion size, multiple lesions, ulcerative morphology, and the percentages of differentiated squamous epithelial and small round cells derived from deep learning-based cytology. Prespecified rule-out and rule-in thresholds were selected in the training set to target 90% sensitivity and 90% specificity, respectively, and then applied to the holdout test set. Results: At the prespecified rule-out threshold (OCNI ≤ 37.6), sensitivity was 92% and negative predictive value was 97%. At the rule-in threshold (OCNI > 60.0), specificity was 89% and positive predictive value was 67%. Calibration was good in the holdout set (intercept, −0.07; slope, 1.13; Hosmer–Lemeshow p = 0.36), and OCNI increased significantly with worsening histopathologic severity. Conclusions: OCNI provided an objective, clinically interpretable estimate of risk for severe OED or OSCC, with strong rule-out and rule-in performance and good calibration. These findings support further external validation of OCNI as an adjunctive tool for oral lesion risk stratification. Full article

Background and Objectives: This study aimed to detect and genotype human papillomavirus (HPV) in tissues from oral squamous cell carcinoma (OSCC), oral potentially malignant disorders (OPMD), and healthy individuals. Materials and Methods: The study included 60 patients (31 men and 29 women; median age 60 years, IQR 41.5–69.8) admitted to the Department of Dental, Oral and Maxillofacial Surgery, Medical University of Sofia. Patients were divided into three groups: healthy oral mucosa (n = 20), OPMD (n = 20), and OSCC (n = 20). HPV was tested using punch biopsies with nested PCR and chip technology. Results: Low-risk HPV was found in four (20%) healthy individuals (types 6/11, 43), seven (35%) OPMD patients (types 6/11, 42, 43), and eleven (55%) OSCC patients (types 6/11, 42, 43). Pairwise comparison showed a significant difference in HPV positivity between healthy individuals and OSCC patients (p = 0.022). Among all HPV-positive OPMDs, the virus was detected in two leukoplakia cases (28.6%), three lichen planus cases (42.9%), one lichenoid lesion case (14.3%), and one proliferative verrucous leukoplakia case (14.3%). According to binary logistic regression, OSCC patients were 4.9 times more likely to be HPV-positive compared to healthy individuals (p = 0.027). Conclusions: HPV infection may play a potential role in the pathogenesis of OPMD and OSCC. Full article

Background: Potentially malignant oral disorders (OPMDs) and oral carcinomas represent a significant oncological concern in incarcerated populations, where multiple modifiable risk factors such as tobacco use, illicit drug consumption, oncogenic human papillomavirus infections, and poor oral hygiene coexist with limited access to preventive and routine dental care. This combination may increase the risk of delayed diagnosis and malignant transformation. Objective: This PRISMA-compliant systematic review aimed to evaluate the prevalence of OPMDs and associated risk factors in prison populations, with a particular focus on identifying gaps in the current evidence. Methods. A systematic literature search was conducted in PubMed, Scopus and Cochrane Library using predefined search strategies. The final search yielded 24 records, which were screened according to PRISMA 2020 guidelines. After title and abstract screening, 10 full-text articles were assessed for eligibility, and 5 cross-sectional studies were included in the qualitative synthesis following independent review. Results: The included studies revealed a substantial burden of oral mucosal lesions in incarcerated populations. Premalignant lesions were reported in a significant proportion of inmates, with oral submucous fibrosis particularly prevalent in some cohorts. Additionally, a high prevalence of oral high-risk HPV infection and widespread oral manifestations were observed. Tobacco use, often combined with betel quid, alcohol, or illicit drugs, emerged as the primary and consistently associated risk factor for oral lesions. Conclusions: Prison populations appear to represent a high-risk group for OPMDs due to the combined effect of behavioral and structural risk factors. However, the limited number of available studies, their cross-sectional design, and methodological heterogeneity prevent definitive conclusions. Further longitudinal and methodologically robust studies are needed to better define prevalence patterns and support targeted screening and prevention strategies in correctional settings. Full article

Background: Oral lichen planus (OLP) is a chronic T-cell-mediated inflammatory disorder classified by the World Health Organization as a potentially malignant disorder. Diagnosis remains challenging due to clinical and histopathological overlap with other oral white lesions, including lichenoid reactions, frictional keratosis, and malignancy. Objectives: This systematic search with narrative review aimed to synthesize current diagnostic criteria, characterize key differential diagnoses, and provide an evidence-based diagnostic framework for clinicians. Methods: A comprehensive literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, and Embase through December 2025. Following a systematic screening process, eligible manuscripts were narratively summarized and a clinical case illustration was demonstrated. Results: Twenty-nine of 214 peer-reviewed studies (including systematic reviews, guidelines, and cohort studies) were summarized. Diagnostic standards have evolved toward the American Academy of Oral & Maxillofacial Pathology (AAOMP) 2016 criteria, which emphasize mandatory clinicopathological associations. Key differential diagnoses include reactive lesions (frictional keratosis), infectious conditions (chronic hyperplastic candidiasis), and other lichenoid patterns. Malignant transformation rates are approximately 1.43%, increasing to 5.13% in the presence of dysplasia, necessitating long-term surveillance. An 81-year-old case exemplifies the value of a stepwise diagnostic approach, in which initial management focuses on the elimination of local irritants and a period of clinical observation, followed by histopathological confirmation of oral lichen planus through biopsy when necessary. Conclusions: Accurate OLP diagnosis requires integrating clinical presentation with histopathological findings. A systematic diagnostic algorithm—incorporating local factor elimination, selective biopsy, and long-term monitoring—is essential to distinguish OLP from its mimics and manage the risk of malignant transformation effectively. Full article

Background/Objectives: Early detection remains the most decisive factor in improving outcomes for oral cancer and oral potentially malignant disorders. However, reliance on conventional biopsy-based pathways presents some practical and biological limitations. This scoping review aimed to map recent advances in non- and minimally invasive diagnostic approaches and to clarify how these innovations are being positioned within clinical workflows. Methods: Following PRISMA-ScR guidance, PubMed/MEDLINE, Scopus, and Web of Science were searched for English-language original studies published between 2020 and 2025. Two independent reviewers screened and charted data on technologies, biomarkers, sampling sources, and clinical applications. Forty-nine studies were included. The literature clustered around four main domains: enhanced cytology (including liquid-based platforms and DNA ploidy analysis), multilayer liquid biopsy strategies (miRNA, cfDNA/ctDNA, methylation panels, and autoantibodies), optical and nanotechnology-based systems (Raman/SERS and sensor platforms), and artificial intelligence-driven decision support tools. Results: Across modalities, a shared emphasis on rapid triage, risk stratification, and follow-up monitoring was evident. Nonetheless, variability in sampling, processing, analytical thresholds, and reporting standards limited cross-study comparability. Conclusions: Recent innovations point toward integrated, panel-based diagnostic models. Broader clinical adoption will require methodological standardization and robust multicenter validation. Full article

Background: Early detection of oral potentially malignant disorders (OPMDs) and early-stage oral squamous cell carcinoma (OSCC) remains a major clinical challenge, as initial lesions often present with subtle or nonspecific findings during conventional white-light examination. Narrow-band imaging (NBI) enhances visualization of mucosal microvasculature and may improve the identification of dysplastic and malignant transformation. Methods: A narrative review of the literature was conducted in the PubMed, Scopus and Google Scholar databases. Studies published between January 2012 and January 2025 evaluating clinical applications of NBI in oral mucosal lesions, OPMDs, or OSCC were included. Results: NBI enhances visualization of intraepithelial papillary capillary loops (IPCLs), whose morphological alterations correlate with epithelial dysplasia and malignant transformation. Evidence suggests high diagnostic sensitivity (up to 87–100%) and specificity (approximately 83–96%) for detecting high-grade dysplasia and early OSCC. NBI also improves biopsy site selection, reduces sampling error, and supports surveillance of high-risk patients. Conclusions: NBI represents a valuable adjunctive diagnostic tool in oral medicine and dentistry. Although it does not replace histopathological examination, its integration into clinical assessment may enhance early cancer detection and improve management of patients with OPMDs. Full article