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PurposeOral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and classification are made possible. Medical imaging technique, computer-aided diagnosis and detection can make potential changes in cancer treatment. In this research work, we have developed a deep learning algorithm for automated, computer-aided oral cancer detecting system by investigating patient hyperspectral images.MethodsTo validate the proposed regression-based partitioned deep learning algorithm, we compare the performance with other techniques by its classification accuracy, specificity, and sensitivity. For the accurate medical image classification objective, we demonstrate a new structure of partitioned deep Convolution Neural Network (CNN) with two partitioned layers for labeling and classify by labeling region of interest in multidimensional hyperspectral image.ResultsThe performance of the partitioned deep CNN was verified by classification accuracy. We have obtained classification accuracy of 91.4% with sensitivity 0.94 and a specificity of 0.91 for 100 image data sets training for task classification of cancerous tumor with benign and for task classification of cancerous tumor with normal tissue accuracy of 94.5% for 500 training patterns was obtained.ConclusionsWe compared the obtained results from another traditional medical image classification algorithm. From the obtained result, we identify that the quality of diagnosis is increased by proposed regression-based partitioned CNN learning algorithm for a complex medical image of oral cancer diagnosis.

Oral squamous cell carcinoma (OSCC) is a malignant disease in the world which has a profound effect on human health and life quality. According to tumor stage and pathological diagnosis, OSCC is mainly treated by combinations of surgery, radiotherapy and chemotherapy. However, traditional treatment methods suffer from some limitations, such as systemic toxicity, limited therapeutic effect and drug resistance. With the rapid development of nanotechnology, nanodrug delivery systems (DDSs) and intelligent DDSs have been widely used in targeted therapy for OSCC. Meanwhile, the newly developed therapeutic techniques such as immunotherapy, gene therapy and bionic technology provide the possibility to realize the active targeted therapy. Here, the latest advances of target therapy for OSCC are reviewed, and their therapeutic remarks, current limits and future prospects are also systematically interpreted. It is believed that active and passive targeted therapies have great potentials for clinical transformation and application of OSCC, which will greatly improve human quality of life.

Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

Human papillomavirus (HPV) is a leading cause of mucosal cancers, including the increasing incidence of HPV-related head and neck cancers. The oral microbiota—a diverse community of bacteria, fungi, and viruses—play a critical role in oral and systemic health. Oral microbiota dysbiosis is increasingly linked to inflammation, immune suppression, and cancer progression. Recent studies have highlighted a complex interaction between HPV and oral microbiota, suggesting this interplay influences viral persistence, immune response and the tumor microenvironment. These interactions hold significant implications for disease progression, clinical outcomes, and therapeutic approaches. Furthermore, the oral microbiota has emerged as a promising biomarker for HPV detection and disease progress assessment. In addition, probiotic-based treatments are gaining attention as an innovative approach for preventing or treating HPV-related cancers by modulating the microbial environment. In this review, current research on the interaction between HPV and oral microbiota is provided, their clinical implications are explored, and the future potential for utilizing microbiota for diagnostic and therapeutic innovations in HPV-associated cancers is discussed.

A complex balanced equilibrium of the bacterial ecosystems exists in the oral cavity that can be altered by tobacco smoking, psychological stressors, bad dietary habit, and chronic periodontitis. Oral dysbiosis can promote the onset and progression of oral squamous cell carcinoma (OSCC) through the release of toxins and bacterial metabolites, stimulating local and systemic inflammation, and altering the host immune response. During the process of carcinogenesis, the composition of the bacterial community changes qualitatively and quantitatively. Bacterial profiles are characterized by targeted sequencing of the 16S rRNA gene in tissue and saliva samples in patients with OSCC. Capnocytophaga gingivalis, Prevotella melaninogenica, Streptococcus mitis, Fusobacterium periodonticum, Prevotella tannerae, and Prevotella intermedia are the significantly increased bacteria in salivary samples. These have a potential diagnostic application to predict oral cancer through noninvasive salivary screenings. Oral lactic acid bacteria, which are commonly used as probiotic therapy against various disorders, are valuable adjuvants to improve the response to OSCC therapy.

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people’s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI’s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.

Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.

The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best-performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79–0.89) and 0.83 (CI 0.78–0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67–0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs.

Resveratrol is known to be an effective chemopreventive phytochemical against multiple tumor cells. However, the increasing drug resistance avoids the cancer treatment in oral cavity cancer. In this study, we investigated the oral antitumor activity of resveratrol and its mechanism in cisplatin-resistant human oral cancer CAR cells. Our results demonstrated that resveratrol had an extremely low toxicity in normal oral cells and provoked autophagic cell death to form acidic vesicular organelles (AVOs) and autophagic vacuoles in CAR cells by acridine orange (AO) and monodansylcadaverine (MDC) staining. Either DNA fragmentation or DNA condensation occurred in resveratrol-triggered CAR cell apoptosis. These inhibitors of PI3K class III (3-MA) and AMP-activated protein kinase (AMPK) (compound c) suppressed the autophagic vesicle formation, LC3-II protein levels and autophagy induced by resveratrol. The pan-caspase inhibitor Z-VAD-FMK attenuated resveratrol-triggered cleaved caspase-9, cleaved caspase-3 and cell apoptosis. Resveratrol also enhanced phosphorylation of AMPK and regulated autophagy- and pro-apoptosis-related signals in resveratrol-treated CAR cells. Importantly, resveratrol also stimulated the autophagic mRNA gene expression, including Atg5, Atg12, Beclin-1 and LC3-II in CAR cells. Overall, our findings indicate that resveratrol is likely to induce autophagic and apoptotic death in drug-resistant oral cancer cells and might become a new approach for oral cancer treatment in the near future.

The crosstalk between the oral microbiome and oral cancer has yet to be characterized. This study recruited 218 patients for clinicopathological data analysis. Multiple types of specimens were collected from 27 patients for 16S rRNA gene sequencing, including 26 saliva, 16 swabs from the surface of tumor tissues, 16 adjacent normal tissues, 22 tumor outer tissue, 22 tumor inner tissues, and 10 lymph nodes. Clinicopathological data showed that the pathogenic bacteria could be frequently detected in the oral cavity of oral cancer patients, which was positively related to diabetes, later T stage of the tumor, and the presence of cervical lymphatic metastasis. Sequencing data revealed that compared with adjacent normal tissues, the microbiome of outer tumor tissues had a greater alpha diversity, with a larger proportion of Fusobacterium , Prevotella , and Porphyromonas , while a smaller proportion of Streptococcus . The space-specific microbiome, comparing outer tumor tissues with inner tumor tissues, suggested minor differences in diversity. However, Fusobacterium , Neisseria , Porphyromonas , and Alloprevotella were more abundant in outer tumor tissues, while Prevotella , Selenomonas , and Parvimonas were enriched in inner tumor tissues. Clinicopathology-specific microbiome analysis found that the diversity was markedly different between negative and positive extranodal extensions, whereas the diversity between different T-stages and N-stages was slightly different. Gemella and Bacillales were enriched in T1/T2-stage patients and the non-lymphatic metastasis group, while Spirochaetae and Flavobacteriia were enriched in the extranodal extension negative group. Taken together, high-throughput DNA sequencing in combination with clinicopathological features facilitated us to characterize special patterns of oral tumor microbiome in different disease developmental stages.