Early Detection Of Oral Cancer Research Articles

Advances in Oral Exfoliative Cytology: From Cancer Diagnosis to Systemic Disease Detection.

Oral exfoliative cytology has emerged as a valuable tool in the early detection of oral cancer and other systemic diseases. This review comprehensively examines the current applications and recent advancements in oral exfoliative cytology techniques. We analyzed published literature from the past decade, focusing on methodological improvements, diagnostic accuracy, and emerging applications. Key findings include: (1) Enhanced cell collection and preparation methods have significantly improved sample quality and diagnostic reliability. (2) Integration of molecular markers and DNA analysis with traditional cytomorphological assessment has increased diagnostic sensitivity and specificity for oral cancer detection. (3) Novel applications in systemic disease detection, including diabetes and iron overload disorders, demonstrate the expanding utility of this technique. (4) Computer-assisted analysis and deep learning algorithms show promise in improving diagnostic accuracy and efficiency. Despite these advancements, challenges remain in standardization and widespread clinical implementation. This review provides a critical evaluation of oral exfoliative cytology's current status and future potential in oral and systemic disease diagnosis.

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.

A systematic review on the early detection of oral cancer using artificial intelligence and electronic tongue technology

This systematic review examines the role of artificial intelligence (AI) and electronic tongue (e-tongue) technology in the early detection of oral cancer. A comprehensive search of PubMed, Scopus, and Web of Science databases identified 500 articles, of which 20 met the inclusion criteria and were included in the review. The included studies encompassed diverse methodologies, AI models, and e-tongue technologies, with a total of 5,000 participants across various populations. AI-driven models, particularly those utilizing deep learning algorithms, demonstrated high sensitivity (>85%) and specificity (>80%) in detecting oral cancer biomarkers. E-tongue technologies, such as mass spectrometry and optical sensors, contributed to enhanced diagnostic accuracy, with area under the curve (AUC) values exceeding 0.85 in several studies. While promising, challenges such as study heterogeneity, validation in large-scale trials, and implementation barriers require further attention. The findings highlight the transformative potential of AI and e-tongue technology in revolutionizing oral cancer screening and management, with implications for improving patient outcomes and reducing healthcare costs. Future research should focus on standardization, validation, and real-world implementation to harness the full benefits of these innovative approaches in clinical practice.

Investigating the Potential of Non-Invasive Breath Test Analysis for Early Detection of Oral Cancer: A Systematic Review

Investigating the Potential of Non-Invasive Breath Test Analysis for Early Detection of Oral Cancer: A Systematic Review

Oral mucosal lesions triage via YOLOv7 models

Background/purposeThe global incidence of lip and oral cavity cancer continues to rise, necessitating improved early detection methods. This study leverages the capabilities of computer vision and deep learning to enhance the early detection and classification of oral mucosal lesions. MethodsA dataset initially consisting of 6903 white-light macroscopic images collected from 2006 to 2013 was expanded to over 50,000 images to train the YOLOv7 deep learning model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red), facilitating efficient triage. ResultsThe YOLOv7 models, particularly the YOLOv7-E6, demonstrated high precision and recall across all lesion categories. The YOLOv7-D6 model excelled at identifying malignant lesions with notable precision, recall, and F1 scores. Enhancements, including the integration of coordinate attention in the YOLOv7-D6-CA model, significantly improved the accuracy of lesion classification. ConclusionThe study underscores the robust comparison of various YOLOv7 model configurations in the classification to triage oral lesions. The overall results highlight the potential of deep learning models to contribute to the early detection of oral cancers, offering valuable tools for both clinical settings and remote screening applications.

A Comprehensive Review of Advancements in Diagnostic Imaging: Unveiling Oral Cavity Malignancies Using Computed Tomography.

Early detection of oral cavity malignancies is essential for improving treatment outcomes and patient survival rates. Diagnostic imaging, particularly computed tomography (CT), plays a pivotal role in the early identification and detailed assessment of these malignancies. This comprehensive review explores the advancements in CT imaging and its application in diagnosing oral cavity cancers. It discusses the anatomy and physiology of the oral cavity, the clinical characteristics of common malignancies, and the principles and protocols of CT imaging. The review highlights the diagnostic features of oral malignancies on CT, including distinguishing benign from malignant lesions and staging criteria. Emerging technologies, such as higher-resolution imaging, integration with other modalities, and the potential of artificial intelligence, are examined for their role in enhancing diagnostic accuracy. The clinical implications, challenges, and future directions in the use of CT imaging for oral cavity malignancies are also discussed. This review underscores the importance of continued research and technological advancements in optimizing the use of CT for early detection and effective management of oral cavity cancers.

Assistance of Artificial Intelligence in the Early Detection of Oral Cancer is Transforming Diagnosis Methods

Assistance of Artificial Intelligence in the Early Detection of Oral Cancer is Transforming Diagnosis Methods

Oral Cancer Risk and Screening Prevalence Among Hospitalized Women.

Background Oral cancer screenings are often on the back burner in the face of other cancer screenings. In high-risk individuals, early detection of oral cancer has a better prognosis and survival. Hospitalization may offer an opportunity to target high-risk populations. This study evaluates the prevalence of women at high risk for oral cancer among hospitalized women and their preference for oral cancer screening. Design and participants Five hundred and ten cancer-free women admitted to the hospital under the internal medicine service at an academic center were enrolled to participate in the study. Three hundred and seventy women were at high risk for developing oral cancer, defined by smoking status, alcohol use, or both. High-risk women received bedside smoking cessation counseling and oral cancer informational handouts and were offered oral screening examinations during hospitalization. Six months after discharge, study participants received a follow-up phone call to determine if these women discussed oral cancer screening with their primary care physicians at the follow-up visit. Results Seventy-three percent of the hospitalized women were at high risk for developing oral cancer. Fifty-seven percent of high-risk women reported having no primary dentist. High-risk women were more likely to be younger, reported a disability, and had a lower comorbidity burden than the average-risk group. Only 41% of high-risk hospitalized women received oral cancer screening examinations during the hospital stay. Post-hospitalization, 66% of high-risk patients discussed oral cancer screening with their primary care. Conclusion Almost three-fourths of hospitalized women are at high risk for developing oral cancer. Hospitalization provides an opportunity to educate and screen high-risk populations.

Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network

Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network

Awareness of Oral Cancer among Patients at a Tertiary Dental Care Center

Abstract Objective Awareness of etiology and clinical presentation of oral cancer among general population would help in the early detection of oral cancer and a favorable outcome for the patients. The aim of the present study was to assess the awareness regarding oral cancer among patients of a tertiary dental care center. Material and Methods A cross-sectional descriptive survey was conducted among patients of a tertiary dental care center using a self-administered questionnaire comprising of 18 questions to assess the awareness related to risk factors, clinical presentation, and treatment options. Data was entered in MS Excel and analyzed using SPSS software version 28. Descriptive statistics was used to summarize the data and percentage of patients responding to various options in questions. Association between variables was assessed by chi-square test. A p-value of < 0.05 was considered significant. Results A total of 325 dental patients responded to the questionnaire. Tobacco smoking and chewing were identified well as risk factors by almost all respondents; however, alcohol, human papillomavirus, and poor oral hygiene were less identified. Statistically significant differences were observed in the people's awareness of oral cancer based on various sociodemographic variables. Conclusion The patients had a basic awareness of oral cancer. However, they had limited knowledge on risk factors other than tobacco. Knowledge has to be improved on risk factors other than tobacco. Patient's level of awareness also varied based on socioeconomic differences among them. Oral cancer awareness programs conducted at the primary health center level may ensure proper awareness in the general population which could enable prevention and/or early detection of such conditions.

To Estimate and Compare the Level of Serum Immunoglobulin A in Patients of Oral Submucous Fibrosis, Leukoplakia, and Control Group

ABSTRACT Aim: To estimate and compare the level of serum immunoglobulin A in patients of oral submucous fibrosis (OSMF), leukoplakia, and control group. Introduction: OSMF is a well-recognized potentially malignant condition in the oral cavity, and a transformation rate as high as 7.6% over a period of 10 years has been reported from India. Leukoplakia has evolved as a clinico-pathologic concept over many years, with the current clinical designation being accepted worldwide. Materials and Methods: Quantia IgA is a turbidometric immunoassay for the detection of IgA in human serum and is based on the principle of agglutination reaction. The test specimen is mixed with the activation buffer (R1) and then with antihuman IgA reagent (R2) and allowed to react. For the estimation of serum IgA, 500 μl of Quantia IgA activation buffer R1 is taken in a clean test tube. Serum sample is diluted in the ratio of 1:5 with normal saline. Ten microliters of diluted serum sample are added to R1. After incubation for 10 min, 50 μl of R2 is added, which is Quantia IgA antihuman IgA reagent to the sample, and the reading is recorded at wavelength 340 nm at 37°C. The level of IgA will be estimated by a semi-automatic biochemical analyzer machine and then data analysis will be done. Results: The immunologic alterations observed by us in OSMF and leukoplakia are almost similar or nearer to the alterations observed in oral cancer, so it is reasonable to assume that OSMF and leukoplakia can be an intermediate stage in the transformation process of a normal cell to oral malignancy. Conclusion: Prevention and early detection is the best way to reduce the incidence and mortality of premalignant disorders and oral cancer. There are many ways and methods of early detection of premalignant disorders and oral cancer, and the immunological method is one of them.

How good is ChatGPT at answering patients’ questions related to early detection of oral (mouth) cancer?

ObjectivesTo examine the quality, reliability, readability, and usefulness of ChatGPT in promoting oral cancer early detection. Study design108 patients-oriented questions about oral cancer early detection were compiled from expert panel, professional societies, and web-based tools. Questions were categorized into four topic domains and ChatGPT 3.5 was asked each question independently. ChatGPT answers were evaluated regarding quality, readability, actionability, and usefulness using. Two experienced reviewers independently assessed each response. ResultsQuestions related to clinical appearance constituted 36.1% (n=39) of the total questions. ChatGPT provided “very useful” responses to the majority of questions (75%; n=81). The mean Global Quality Score was 4.24 ±1.3 out of 5. The mean reliability score was 23.17 ± 9.87 out of 25. The mean understandability score was 76.6% ±25.9 out of 100, while the mean actionability score was 47.3% ± 18.9 out of 100. The mean FKS reading ease score was 38.4%± 29.9, while the mean SMOG index readability score was 11.65 ± 8.4. No misleading information was identified among ChatGPT responses. ConclusionChatGPT is an attractive and potentially useful resource for informing patients about early detection of oral cancer. Nevertheless, concerns do exist about readability and actionability of the offered information.

Screening of Oral Squamous Cell Carcinoma Through Color Intensity-Based Textural Features.

Background Early screening and diagnosis of oral squamous cell carcinoma (OSCC) has always been a major challenge for pathologists. Artificial intelligence (AI)-assisted screening tools can serve as an adjunct for the objective interpretation of Papanicolaou (PAP)-stained oral smears. Aim This study aimed to develop a handy and sensitive computer-assisted AItool based on color-intensity textural features to be applied to cytologic images for screening and diagnosis of OSCC. Methodology The study included two groups consisting of 80 OSCC subjects and 80 control groups. PAP-stained smears were collected from both groups. The smears were analyzed in Matlab software computed data and color intensity-based textural features such as entropy, contrast, energy, homogeneity, and correlation,were quantitatively extracted. Results In this study, a statistically significant difference was noted for entropy, energy, correlation, contrast, and homogeneity. It was found that entropy and contrast were found to be higher with a decrease in homogeneity, correlation, and energy in OSCC when compared to the control group. Receiver operating characteristiccurve analysis was done and accuracy, sensitivity, and specificity were found to be 88%, 91%, and 81%, respectively. Conclusion The gray-level co-occurrence matrix (GLCM) color intensity-based textural features play a significant role in differentiating dysplastic and normal cells in the diagnosis of OSCC. Computer-aided textural analysis has the potential to aid in the early detection of oral cancer, which can lead to improved clinical outcomes.

Unveiling the Potential of VELScope in Potentially Malignant Oral Lesion Early Detection for Future Dental Diagnosis Technology

Introduction: Oral lesions range from innocuous to possibly dangerous, including oral cancer, which is the world's 16th most common malignancy. Early detection of oral cancer can enhance a patient's life expectancy by a decade, emphasizing the need of diagnostic instruments like VELScope. VELScope is a portable device that employs blue light to examine oral tissue, exposing natural fluorescence and emphasizing aberrant regions that would otherwise be invisible without the use of specialized equipment. Objectives: This review looks at the possibility of the VELScope for identifying oral lesions, since it has the potential to be a future dental diagnostic tool. Method: We began with 88 findings and reduced them down to 33 research regarding VELScope technology published in the previous five years. Following screening, we chose ten articles using the same criteria for all research. Results: VELScope uses fluorescence to identify both obvious and concealed oral lesions, which is especially useful when collagen loss occurs in normal tissues. While using autofluorescence requires knowledge, it assists in analyzing margins in probable pre- malignant oral conditions. VELScope, despite its limitations, has promise for early cancer diagnosis as technology develops. Conclusion: When utilized for early diagnosis, VELScope helps to reduce oral cancer mortality, reaching an 83% 5-year survival rate. While it has limits, continuous technical improvements have the potential to improve its accuracy, making it a vital tool for accurate early diagnosis and lowering oral cancer mortality.

Improving detection of oral lesions: Eye tracking insights from a randomized controlled trial comparing standardized to conventional approach.

Early detection of oral cancer (OC) or its precursors is the most effective measure to improve outcome. The reasons for missing them on conventional oral examination (COE) or possible countermeasures are still unclear. In this randomized controlled trial, we investigated the effects of standardized oral examination (SOE) compared to COE. 49 dentists, specialists, and dental students wearing an eye tracker had to detect 10 simulated oral lesions drawn into a volunteer's oral cavity. SOE had a higher detection rate at 85.4% sensitivity compared to 78.8% in the control (p = 0.017) due to higher completeness (p < 0.001). Detection rate correlated with examination duration (p = 0.002). A standardized approach can improve systematics and thereby detection rates in oral examinations. It should take at least 5 min. Perceptual and cognitive errors and improper technique cause oral lesions to be missed. Its wide implementation could be an additional strategy to enhance early detection of OC.

Prevalence and risk factors of oral potentially malignant disorders in Indonesia: a cross-sectional study undertaken in 5 provinces

Detection of subjects with oral potentially malignant disorders in a population is key to early detection of oral cancer (OC) with consequent reduction of cancer-related morbidity and mortality. Our aim was to investigate the prevalence and associated risk factors for OPMD in representative provinces of Indonesia. This cross-sectional study was undertaken in five Indonesian provinces: West Java (WJ), Jakarta (JKT), West Papua (WP), West Kalimantan (WK) and Banda Aceh (BA). Respondents answered a previously validated questionnaire including information on ethnicity, occupation, socioeconomic status (SES), oral health practices, and behaviours associated with oral cancer. An oral examination was undertaken using WHO standardized methodology. Data were analysed using ANOVA, Chi-Square, and logistic regression to assess association between risk factors and mucosal disease. A total of 973 respondents between the ages of 17 and 82 years was enrolled (WJ 35.5%,JKT 13.3% WP 18.3%, WK 9%, BA 23.9%). Tobacco smoking (14.8%), Betel quid (BQ) chewing (12.6%) and alcohol drinking (4%) varied geographically. A well-established OPMD was detected in 137 (14.1%) respondents and 2 (0.2%) presented with chronic ulceration later diagnosed as OC. Leukoplakia was the most common OPMD found (9.7%), while the prevalence of oral submucous fibrosis (OSMF), not previously described in the nation, was 2.3%. Poor knowledge of OC risk factors, poor oral hygiene behaviours, low-income SES and ethnicity were significantly associated with the presence of an OPMD. There is a previously under-reported high prevalence of OPMD in Indonesia. Overall, we found a strong correlation between the presence of an OPMD and individual habituation to known risk factors.

Salivary Biomarkers for Oral Cancer Detection: An Exploratory Systematic Review.

Different efforts have been made to find better and less invasive methods for the diagnosis and prediction of oral cancer, such as the study of saliva as a source of biomarkers. The aim of this study was to perform a scoping review about salivary molecules that have been assessed as possible biomarkers for the diagnosis of oral squamous cell carcinoma (OSCC). A search was conducted using EBSCO, PubMed (MEDLINE), Scopus, and Web of Science. The research question was as follows: which molecules present in saliva have utility to be used as biomarkers for the early detection of oral cancer? Sixty-two studies were included. Over 100 molecules were assessed. Most of the markers were oriented towards the early diagnosis of OSCC and were classified based on their ability for detecting OSCC and oral potentially malignant disorders (OPMDs), OSCC outcome prediction, and the prediction of the malignant transformation of OPMDs. TNF-α, IL-1β, IL-6 IL-8, LDH, and MMP-9 were the most studied, with almost all studies reporting high sensitivity and specificity values. TNF-α, IL-1β, IL-6 IL-8, LDH, and MMP-9 are the most promising salivary biomarkers. However, more studies with larger cohorts are needed before translating the use of these biomarkers to clinical settings.

Enhancing deep learning classification performance of tongue lesions in imbalanced data: mosaic-based soft labeling with curriculum learning

BackgroundOral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reducing cancer-specific morbidity and mortality. Recently, there have been studies to apply the rapidly advancing technology of deep learning for diagnosing oral cavity cancer and OPMDs. However, several challenging issues such as class imbalance must be resolved to effectively train a deep learning model for medical imaging classification tasks. The aim of this study is to evaluate a new technique of artificial intelligence to improve the classification performance in an imbalanced tongue lesion dataset.MethodsA total of 1,810 tongue images were used for the classification. The class-imbalanced dataset consisted of 372 instances of cancer, 141 instances of OPMDs, and 1,297 instances of noncancerous lesions. The EfficientNet model was used as the feature extraction model for classification. Mosaic data augmentation, soft labeling, and curriculum learning (CL) were employed to improve the classification performance of the convolutional neural network.ResultsUtilizing a mosaic-augmented dataset in conjunction with CL, the final model achieved an accuracy rate of 0.9444, surpassing conventional oversampling and weight balancing methods. The relative precision improvement rate for the minority class OPMD was 21.2%, while the relative F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} score improvement rate of OPMD was 4.9%.ConclusionsThe present study demonstrates that the integration of mosaic-based soft labeling and curriculum learning improves the classification performance of tongue lesions compared to previous methods, establishing a foundation for future research on effectively learning from imbalanced data.

Oral carcinogenesis and non-invasive biomarkers for the diagnosis of oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is the most prevalent cancer in Pakistani population because of consumption of different tobacco-containing products whether smoked or chewed. These patients commonly report at a late stage of the disease. The patient's survival only depends upon early-stage diagnosis. Literature has reported that there is an increased tendency of transformation of oral potentially malignant disorder (OPMD) into OSCC. Biopsy is the gold standard measure for diagnosis but for OPMD cases biopsy was not recommended and most of the times the patients were also not willing to have a biopsy done. So, along with the biopsy there is a need for non-invasive protein biomarker that might aid in the early detection of oral cancer as well as highlight the high-risk individuals. This short communication focuses on the role of early diagnostic biomarkers present in literature, such as synuclein-γ (SNCG), Squamous cell carcinoma antigen (SCCAg), p53, MMPs-12, and IL-6. Furthermore, application of these biomarkers in multi-centre longitudinal studies is needed to establish their role as a non-invasive diagnostic biomarker for OSCC.

Assessment of Awareness and Knowledge of Oral Potentially Malignant Disorders and Oral Cancer among Accredited Social Health Activist Workers from Randomly Selected Blocks of Thrissur District, Kerala, India: A Cross Sectional Study

Introduction: Oral and oropharyngeal cancers are prevalent in the head-and-neck region, with global rates decreasing. However, in India, they are rising due to a lack of knowledge about risk factors and symptoms. The Indian government launched the National Health Mission to provide accessible, affordable, and reliable primary health care. Accredited social health activist (ASHA) workers can link health services to rural people. This study aimed to evaluate the awareness level among these workers about potentially malignant oral disorders and oral cancer (OC). Materials and Methods: A predesigned semi-structured questionnaire-based descriptive cross-sectional study was conducted among 150 voluntary ASHA workers from randomly selected blocks of Thrissur district, Kerala, to collect the data. Results: Our study showed that the majority of ASHA workers (82.3%) were aware of the role of tobacco in OC. A substantial portion of workers (88.4%) expressed that early detection of OC is possible at its initial stage. Less than 10% (7.5%) of all the ASHA workers were educated by dentists, whereas almost three-quarters (75.5%) of ASHAs preferred primary health center health practitioners as primary representatives for early OC detection, despite their being poorly trained in the early diagnosis and detection of OC. Conclusion: India has made progress in controlling malignancies but still lags in reducing OC mortality rates. Spreading knowledge of contributory agents and early detection methods through ASHA workers could reduce the incidence and mortality of OC. Including more dentists in the public health system and training ASHA workers on early OC detection can play a major role in reducing its incidence.