Diagnosis Of Oral Cancer Research Articles

Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis

ObjectiveOral cancer is a widespread global health problem characterised by high mortality rates, wherein early detection is critical for better survival outcomes and quality of life. While visual examination is the primary method for detecting oral cancer, it may not be practical in remote areas. AI algorithms have shown some promise in detecting cancer from medical images, but their effectiveness in oral cancer detection remains Naïve. This systematic review aims to provide an extensive assessment of the existing evidence about the diagnostic accuracy of AI-driven approaches for detecting oral potentially malignant disorders (OPMDs) and oral cancer using medical diagnostic imaging.MethodsAdhering to PRISMA guidelines, the review scrutinised literature from PubMed, Scopus, and IEEE databases, with a specific focus on evaluating the performance of AI architectures across diverse imaging modalities for the detection of these conditions.ResultsThe performance of AI models, measured by sensitivity and specificity, was assessed using a hierarchical summary receiver operating characteristic (SROC) curve, with heterogeneity quantified through I2 statistic. To account for inter-study variability, a random effects model was utilized. We screened 296 articles, included 55 studies for qualitative synthesis, and selected 18 studies for meta-analysis. Studies evaluating the diagnostic efficacy of AI-based methods reveal a high sensitivity of 0.87 and specificity of 0.81. The diagnostic odds ratio (DOR) of 131.63 indicates a high likelihood of accurate diagnosis of oral cancer and OPMDs. The SROC curve (AUC) of 0.9758 indicates the exceptional diagnostic performance of such models. The research showed that deep learning (DL) architectures, especially CNNs (convolutional neural networks), were the best at finding OPMDs and oral cancer. Histopathological images exhibited the greatest sensitivity and specificity in these detections.ConclusionThese findings suggest that AI algorithms have the potential to function as reliable tools for the early diagnosis of OPMDs and oral cancer, offering significant advantages, particularly in resource-constrained settings.Systematic Review Registrationhttps://www.crd.york.ac.uk/, PROSPERO (CRD42023476706).

Assessment of Dental Students' Knowledge and Attitudes About the Epidemiologic and Clinical Characteristics of Oral Cancer: Implications for Other Middle-income Countries.

The stage of detection of head and neck cancer and the time between detection and treatment are critical to prognosis. The importance of the dentist in primary diagnosis and treatment planning has been emphasized. The purpose of this study was to evaluate the knowledge of dental students in the prevention and early diagnosis of oral cancer. A descriptive cross-sectional study was conducted on 199 students from different institutions. A self-administered questionnaire with 34 multiple-choice questions on clinical and epidemiologic knowledge and opinions about oral cancer was used. Descriptive analysis to show means and frequencies and the binomial logistic regression test were used to perform the statistical analysis. The confidence level used was 95%. The results showed that all students in the study had heard of the existence of oral cancer, but only 24.6% knew someone with the disease. The majority reported a lack of information in the population about prevention and self-examination, as well as a lack of information campaigns in institutions. Although the majority perform clinical examinations of the oral mucosa (73.4%) and refer patients with suspicious lesions to specialists, there is low confidence in performing biopsies (73.4%) and gaps in training during graduation (84.4%). The majority have never attended continuing education courses on oral cancer but show interest (97.5%). The study identifies deficiencies in the knowledge and practices of dental students and highlights the need for improved teaching and training to promote oral cancer prevention and early detection. Further research in this area and ongoing assessment of students' skills are suggested.

Assessment of optical coherence tomography and optical coherence microscope for oral cancer diagnosis: a clinical accuracy study

Objective: To explore and evaluate the feasibility and accuracy of using optical coherence tomography and optical coherence microscope (OCT/OCM) for diagnosis of oral cancer. Methods: In this study, OCT/OCM was utilized to image the oral mucosa specimens. A total of 289 ex vivo oral mucosa specimens were collected from 68 patients with oral cancer who were hospitalized at Department of Oral and Maxillofacial Head and Neck Tumors, Capital Medical University School of Stomatology, between January 2021 and February 2023, resulting in a dataset of 1 445 OCT/OCM images. By observing the characteristic patterns in the OCT/OCM images, including normal oral mucosa, epithelial abnormal proliferation (mild, moderate, severe), and oral cancer, these patterns were matched with corresponding pathological images. A diagnostic study was conducted, employing pathological diagnosis as the gold standard and utilizing a double-blind experimental design involving three diagnostic evaluators who participated in the analysis and diagnosis of OCT/OCM images. Results: The OCT/OCM images demonstrated good correlation with the corresponding pathological images, and diagnostic criteria were established based on the comparative results. In the diagnostic study involving three investigators, the accuracy was 82%, sensitivity was 84% (95%CI: 80%-88%), and specificity was 81% (95%CI: 77%-85%). There was a high level of agreement among the observers (κ=0.614), indicating substantial concordance in the diagnostic results among the three investigators. Conclusions: This study demonstrates the potential of OCT/OCM for diagnosis of oral cancer. The technology accurately distinguishes between normal oral mucosa, epithelial abnormal proliferation and oral cancer.

Assessing Artificial Intelligence in Oral Cancer Diagnosis: A Systematic Review

Background: With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures. Aim: The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024. Methodology: With an emphasis on AI applications in oral cancer diagnostics, a thorough search approach was used to find pertinent publications published between 2020 and 2024. Using particular keywords associated with AI, oral cancer, and diagnostic imaging, databases such as PubMed, Scopus, and Web of Science were searched. Among the selection criteria were actual English-language research papers that assessed the effectiveness of AI models in diagnosing oral cancer. Three impartial reviewers extracted data, evaluated quality, and compiled the findings using a narrative synthesis technique. Results: Twelve papers that demonstrated a range of AI applications in the diagnosis of oral cancer satisfied the inclusion criteria. This study showed encouraging results in lesion identification and prognostic prediction using machine learning and deep learning algorithms to evaluate oral pictures and histopathology slides. The results demonstrated how AI-driven technologies might enhance diagnostic precision and enable early intervention in cases of oral cancer. Conclusion: Unprecedented prospects to transform oral cancer diagnosis and detection are provided by artificial intelligence. More resilient AI systems in oral oncology can be achieved by joint research and innovation efforts, even in the face of constraints like data set variability and regulatory concerns.

Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease

AimDue to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease. MethodsFour Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach. ResultsThese three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity. ConclusionPredictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.

A bioinspired porous and electroactive reduced graphene oxide hydrogel based biosensing platform for efficient detection of tumor necrosis factor-α.

Oral cancer is one of the leading cancer types, which is frequently diagnosed at an advanced stage, giving patients a poor prognosis and fewer therapeutic choices. To address this gap, exploiting biosensors utilizing anti-biofouling hydrogels for early-stage oral cancer detection in non-invasive body fluids is gaining utter importance. Herein, we have demonstrated the fabrication of an innovative electrochemical immunosensor for the rapid, label-free, non-invasive, and affordable detection of tumor necrosis factor-α (TNF-α), a biomarker associated with oral cancer progression in artificial saliva samples. The gold screen-printed electrodes (gSPEs) are modified with a green synthesized porous and electroactive reduced graphene oxide (rGO) hydrogel utilizing L-cystine (L-cys) as both in situ reducing and surface functionalization agent, followed by covalent immobilization of anti-TNF-α and blocking of residual sites with bovine serum albumin (BSA) to fabricate the BSA/anti-TNF-α/L-cys_rGO hydrogel/gSPE immunosensing platform. The fabricated platform demonstrates excellent performance, with a low limit of detection of 1.20 pg mL-1, a broad linear range from 1 to 200 pg mL-1, and a high sensitivity of 2.10 μA pg-1 mL cm-2 carried out with differential pulse voltammetry (DPV) technique. Moreover, it exhibits specificity towards TNF-α, even in the presence of potential interferents and other cancer biomarkers. Besides, the biosensor showed good reproducibility and repeatability with a relative standard deviation (%RSD) of 5.11% and 1.85%, respectively. Thus, integrating the L-cys_rGO hydrogel in the immunosensor design offers enhanced performance, paving the way for its application in early-stage oral cancer diagnosis.

Target Device Specific Optimized Deep Learning Model For Oral Cancer Diagnosis

Target Device Specific Optimized Deep Learning Model For Oral Cancer Diagnosis

Booming Artificial Intelligence in Oral Cancer Diagnosis: From Image Processing to Stage Classification.

Booming Artificial Intelligence in Oral Cancer Diagnosis: From Image Processing to Stage Classification.

Exploring the role of artificial intelligence in oral cancer diagnosis: Review

Oral cancer (OC) is the most common forms of head and neck cancer. Even with advancing research it still continues to have lowest survival rate globally. Histopathologiy still remains the gold standard for oral cancer diagnosis. But, it has few drawbacks being prone to errors (variability) and is time consuming leading to delay in diagnosis. In order to overcome these pitfalls there is a need for alternative diagnostic approaches which are faster and accurate at the same time. Considerable efforts have been focused on exploring the role of artificial intelligence (AI) in medical diagnostics in recent times. The role of AI in medical imaging and diagnosis seems promising. Key benefit of AI is better accuracy, rapid diagnosis and reduced manual visualization of slides. This review article focuses on the role of AI in oral cancer detection with emphasis on machine learning and deep learning methods, decision support systems. Future challenges, benefits and limitations of AI have also been discussed.

Precision and speed: The AI revolution in oral squamous cell carcinoma detection

Oral Squamous Cell Carcinoma (OSCC) is a highly aggressive tumor with a poor prognosis and is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. AI can precisely analyze a vast dataset of various imaging modalities, such as fluorescent, hyperspectral, cytological, histological, radiological, endoscopic, clinical, and infrared thermal modalities. In this review, we discuss digital histopathological image analysis, computer vision and radiological analysis for the early detection of OSCC.

Association of serum and salivary dipeptidyl peptidase-4 (DPP-4) with oral cancerous and precancerous lesions; an observational diagnostic accuracy study

BackgroundEnhancing the prognosis and treatment outcomes of oral cancer relies heavily on its early detection. This study aims to establish a connection between serum and salivary dipeptidyl peptidase-4 (DPP-4) levels and oral squamous cell carcinoma (OSCC), comparing them with oral potentially malignant lesions (OPMLs) and control subjects and validating salivary DPP-4 as a diagnostic marker for the early detection of oral cancer.MethodologyForty-five systemically healthy individuals were categorized into three groups: Group I consisted of 15 patients diagnosed with OSCC, Group II comprised 15 patients with OPMLs (including leukoplakia and oral lichen planus), and Group III included 15 participants without any oral mucosal lesions. Serum and whole unstimulated salivary samples were collected from all participants to assess DPP-4 levels using an enzyme-linked immunosorbent assay (ELISA) kit. Receiver operating characteristic (ROC) analysis was conducted to determine the area under the curve (AUC), sensitivity, specificity, and diagnostic accuracy of DPP-4.ResultsBoth serum and salivary DPP-4 levels were highest in the healthy group, followed by OPMLs, and lowest in the OSCC group, with statistically significant differences observed. ROC analysis demonstrated excellent diagnostic accuracy of salivary DPP-4 in distinguishing OSCC from healthy individuals, OPMLs from healthy individuals, and OSCC from OPMLs, with accuracies of 100%, 100%, and 96.67%, respectively. Salivary DPP-4 levels also exhibited a statistically significant correlation with OSCC grades.ConclusionsDPP-4 appears to play a protective, anti-oncogenic role in maintaining oral tissue health. The remarkable diagnostic accuracy of both serum and salivary DPP-4 in discriminating between OSCC, OPMLs, and healthy controls suggests its potential utility as a well-established marker for early oral cancer diagnosis. Salivary DPP-4, being non-invasive, could serve as a convenient chair-side diagnostic tool for the early detection of OSCC.Clinical trial registrationThe study was retrospectively registered on clinicaltrial.gov with NCT06087042, date of registration (first posted date): 17/10/2023

Development of a telemedicine network for early oral cancer diagnosis: the Argentine Patagonia experience—a perspective through a pilot study

Development of a telemedicine network for early oral cancer diagnosis: the Argentine Patagonia experience—a perspective through a pilot study

Application of Targeted Optical Coherence Tomography in Oral Cancer: A Cross-Sectional Preliminary Study.

The diagnosis of oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) represent a significant challenge in oral medicine. Optical coherence tomography (OCT) shows promise for evaluating oral tissue microstructure but lacks standardized diagnostic protocols tailored to the structural variability and lesions of oral mucosa. This cross-sectional observational study aims to evaluate the diagnostic accuracy of targeted biopsy-based and site-coded OCT protocols for common OPMDs and OSCC. Adult patients clinically diagnosed with OPMDs, including oral leukoplakia (OL), oral lichen planus (OLP), and OSCC were enrolled. Clinical and OCT evaluation before and after punch scalpel-site registration preceding diagnostic biopsy on the target site was performed. Blinded observers analyzed the OCT scans for OCT-based diagnoses. Sensitivity, specificity, and diagnostic accuracy for OCT evaluations before and after punch scalpel-site registration were statistically compared with histological findings. A dataset of 2520 OCT scans and 210 selected images from 21 patients was obtained. Sensitivity and specificity post-target site registration were high for OSCC (98.57%, 100.00%), OL (98.57%, 98.57%), and OLP (97.14%, 98.57%). The positive predictive values ranged from 97.14% to 100.00%, while negative predictive values ranged from 98.57% to 99.29%. Inter-observer agreements were strong for OSCC (0.84) and moderate for OL (0.54) and OLP (0.47-0.49). Targeted OCT scans significantly improved diagnostic accuracy for all conditions (p < 0.001). This preliminary study supports using site-targeted OCT scans followed by a site-targeted punch biopsy, enhancing precision in oral diagnostics. This approach is foundational for developing pioneering automated algorithms guiding oral cancer and pre-cancer diagnosis via OCT imaging.

Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence.

Introduction: Optical Coherence Tomography (OCT) has emerged as an important imaging modality in non-invasive diagnosis for oral cancer and can provide real-time visualisation of tissue morphology with the required high resolution. This systematic review aims to assess the diagnostic accuracy of OCT in the detection of oral cancers, and to explore the potential integration of OCT with artificial intelligence (AI) and other imaging techniques to enhance diagnostic precision and clinical outcomes in oral healthcare. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, Google Scholar, Cochrane Central Register, and Web of Science from inception until August 2024. Studies were included if they employed OCT for oral cancer detection, reported diagnostic outcomes, such as sensitivity and specificity, and were conducted on human subjects. Data extraction and quality assessment were performed independently by two reviewers. The synthesis highlights advancements in OCT technology, including AI-enhanced interpretations. Results: A total of 9 studies met the inclusion criteria, encompassing a total of 860 events (cancer detections). The studies spanned from 2008 to 2022 and utilised various OCT techniques, including clinician-based, algorithm-based, and AI-driven interpretations. The findings indicate OCT's high diagnostic accuracy, with sensitivity ranging from 75% to 100% and specificity from 71% to 100%. AI-augmented OCT interpretations demonstrated the highest accuracy, emphasising OCT's potential in early cancer detection and precision in guiding surgical interventions. Conclusions: OCT could play a very prominent role as a new diagnostic tool for oral cancer, with very high sensitivity and specificity. Future research pointed towards integrating OCT with other imaging methods and AI systems in providing better accuracy of diagnoses, plus more clinical usability. Further development and validation with large-scale multicentre trials is imperative for the realisation of this potential in changing the way we practice oral healthcare.

The Role of Ryanodine Receptor 2 Polymorphisms in Oral Squamous Cell Carcinoma Susceptibility and Clinicopathological Features.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, and oral squamous cell carcinoma (OSCC) is one of the most common types. There is strong evidence that ryanodine receptor 2 (RYR2) plays an important role in different types of cancer according to previous studies. Its expression is associated with survival in patients with HNSCC, but it is unknown whether altered RYR2 expression contributes to tumorigenesis. Therefore, we examined how RYR2 polymorphisms affect OSCC susceptibility and clinicopathological characteristics. Five single nucleotide polymorphisms (SNPs) of RYR2, rs12594, rs16835904, rs2779359, rs3765097, and rs3820216, were analyzed in 562 cases of OSCC and 332 healthy controls using real-time PCR. We demonstrated that RYR2 SNP rs12594 was significantly different between the case and control groups, but this difference was not significant after adjusting for personal habits. In contrast, we found that different genotypes of SNP rs2779359 were significantly associated with the characteristics of clinical stage and tumor size in OSCC patients, according to the odds ratios and the adjusted odds ratios; specifically, patients with the T genotype had 1.477-fold (95% CI, 1.043 to 2.091; p = 0.028) and 1.533-fold (95% CI, 1.087-2.162; p = 0.015) increases in clinical stage and tumor size, respectively, compared with patients with the C allele. The results of our study, in which RYR2 SNPs associated with OSCC progression and development were examined for the first time, suggest that clinicopathological characteristics may alter OSCC susceptibility. Finally, RYR2 SNP rs2779359 not only plays a role in both the prognosis and diagnosis of oral cancer but is also likely an important predictive factor for recurrence, response to treatment, and medication toxicity.

Emerging Diagnostic Modalities in Oral Cancer Detection and Management

Oral cancer remains a significant global health concern, yet it is often detected at an advanced stage. The limitations of traditional diagnostic techniques have prompted increased research efforts towards the development of more efficient and early detection methods. Recent advancements in oral cancer diagnosis include the use of salivary biomarkers, optical imaging, liquid biopsy, advanced imaging, and AI algorithms. These non-invasive and painless sampling methods have shown high sensitivity and specificity, particularly in the case of salivary biomarkers. Clinical trials and cooperation are necessary to demonstrate the effectiveness of these technologies and gain approval from relevant authorities and acceptance among clinicians. This review highlights the potential of these new modalities in transforming the approach to oral cancer diagnosis, leading to early detection, accurate diagnosis, and quality treatment for patients, ultimately reducing the global burden of this disease.

Knowledge, Attitudes, Practices, and Perceptions of Brazilian Dentists About Oral Cancer.

Dentists play a pivotal role in the early detection of oral cancer. Consequently, they are expected to possess the knowledge and the capability to recognize the features of this disease. The objective of the study is to evaluate dentists from different regions of Brazil regarding their level of knowledge and self-confidence regarding oral cancer. An online self-administered questionnaire was completed by dentists across Brazil registered on the TelessaúdeRS-UFRGS platform. This questionnaire encompassed inquiries related to knowledge, attitudes, and practices regarding oral cancer. A total of 1291 dentists from all regions of the country responded to the questionnaire. The majority of participants were females (75.5%), with an average age of 36.3years, predominantly from the public sector (46.8%). A reasonable level of knowledge regarding oral cancer was observed among dentists, although 48.6% of these professionals felt uncertain about diagnosis procedures. Dentists less than 8years since graduation perceived themselves as more prepared to perform oral cancer diagnoses than those with more experience. Around 55% of participants had never performed a biopsy. Based on the obtained results, it is concluded that continuous education activities focused on oral cancer and implementing practical training during undergraduate studies are imperative. These strategies can improve professionals' self-confidence and diagnostic accuracy, thereby facilitating early disease diagnosis and, consequently, a more favorable prognosis.

The Diagnostic Potential of Non-Invasive Tools for Oral Cancer and Precancer: A Systematic Review.

This systematic review aimed to analyse the published evidence for the use of non-invasive methods for the early detection of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). The literature was systematically searched through several databases: PubMed, Cochrane Library, and Web of Science. Additional exploration was performed through cross-checks on the bibliographies of selected reviews. The inclusion criteria involved studies assessing the application of non-invasive tests on humans in the screening, diagnosis, or surveillance of OSCC or OPMDs and reporting sensitivity (SE) and specificity (SP). The Newcastle-Ottawa scale (NOS) was applied to assess the quality of the studies included. The search strategy resulted in 8012 preliminary records. After a duplicate check, 116 titles remained. After abstract analysis, 70 papers remained. After full text analysis, only 54 of the 70 papers fit the inclusion criteria (28 were original articles and 26 were reviews). Those 26 reviews were used to manually search for further original articles. From this last search, 33 original articles were found. Thus, a total of 61 original studies were included and investigated. Findings from this systematic review indicate useful information, such as a description of the mechanisms, ease of use, limitations, and SE and SP values, to drive the choice of the optimal minimally invasive method to be utilized as an adjunctive tool to examine the suspicious lesions. Each of the analysed tools can be improved or implemented, considering their high SE and low SP. Despite advancements, incisional biopsy continues to be the gold standard for the definitive diagnosis of oral cancer and precancerous lesions. Further research and development are essential to improving the sensitivity, specificity, and reliability of non-invasive tools for widespread clinical application.

Retraction Note: Industry 4.0 transformation: adaptive coati deep convolutional neural network-based oral cancer diagnosis in histopathological images for clinical applications

Retraction Note: Industry 4.0 transformation: adaptive coati deep convolutional neural network-based oral cancer diagnosis in histopathological images for clinical applications

Innovative Progress: Artificial Intelligence in the Realm of Oral Cancer

In recent years, there has been a notable increase in the global incidence of oral cancer, leading to significant morbidity and mortality, especially when diagnosed at advanced stages. The integration: In recent years, there has been a notable increase in the global incidence of oral cancer, leading to significant morbidity and mortality, especially when diagnosed at advanced stages. The integration of technology holds great promise for early detection and diagnosis, facilitating improved patient management for clinicians. The emergence of artificial intelligence (AI) presents a potential breakthrough in oral cancer screening, as it can meticulously analyze vast datasets from various imaging modalities, offering valuable support in the field of oncology. This review focuses on a spectrum of AI techniques utilized for early detection and diagnosis of oral cancer. Additionally, AI techniques may be employed for the effective treatment of oral cancer. Using the abundance of information acquired, this article provides an in-depth overview and discussion of AI's value and benefits in oral cancer screening, early detection, disease prediction, and therapy, among other areas. Furthermore, it identifies present limits and forecasts the hopeful future of AI in oral cancer research.