A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.

Traumatic Neuroma of the Hard Palate Mimicking a Small Salivary Gland Tumor-A Case Report

In the case of any pathologies arising in the hard palate, it is always important to exclude their possible odontogenic origins. Cone-beam computed tomography is mandatory. In cases where a possible non-teeth-related pathology is confirmed, each clinician should remember possible differential diagnostics. Many small salivary glands between the mucosa and bone are present in this palatal area. Both benign and malignant tumors arising from the small glands, and mucosa of the hard palate, might occur. The case presented here mimics a solid tumor because of the nodule consistency. Because of a healthy palatal mucosa without any erosions or irritations with firm attachment to the submucosal nodule, a possible malignant tumor of small salivary gland origins was suspected in this case. When the tumor diameter is small, an excisional biopsy is required to collect good and representative material for further histopathological evaluation. In most cases, bulky nodules present on the palate are hard on palpation, non-movable, and covered with healthy mucosa. Possible bone infiltrations with mucous membrane ulcerations could manifest a more expansive character of the lesion. In the presented case, an unusual occurrence of a traumatic neuroma without any past traumatic etiology of the palate was first differentiated from the occurrence of adenoid-cystic carcinoma (ACC), pleomorphic adenoma, other benign/malignant small gland tumors, or atypical, fibroma/schwannoma of the palate. This paper presents treatment options for this rare oral neural tumor occurrence in the palate and differential diagnosis between hard palate tumors in a 42-year-old male patient, furthermore highlighting the role of an excisional biopsy as a good source for a tissue sample.

New Horizons and Prospects in Oral Cancer Detection

Recent advancements in oral cancer detection prioritize non-invasive and minimally invasive techniques for efficient and accurate screening. This review outlines progress in methods such as narrow band imaging, fluorescence imaging, and optical coherence tomography, showing promise in early lesion detection. Biomarker detection in saliva and targeted nanoparticles enhance early diagnosis, while machine learning improves diagnostic accuracy. However, clinical validation and large-scale studies are needed for widespread adoption.

Automated Analysis of Nuclear Parameters in Oral Exfoliative Cytology Using Machine Learning

BACKGROUND

As oral cancer remains a major worldwide health concern, sophisticated diagnostic tools are needed to aid in early diagnosis. Non-invasive methods like exfoliative cytology, albeit with the help of artificial intelligence (AI), have drawn additional interest.

AIM

The study aimed to harness the power of machine learning algorithms for the automated analysis of nuclear parameters in oral exfoliative cytology. Further, the analysis of two different AI systems, namely convoluted neural networks (CNN) and support vector machine (SVM), were compared for accuracy.

METHODS

A comparative diagnostic study was performed in two groups of patients (n=60). The control group without evidence of lesions (n=30) and the other group with clinically suspicious oral malignancy (n=30) were evaluated. All patients underwent cytological smears using an exfoliative cytology brush, followed by routine Hematoxylin and Eosin staining. Image preprocessing, data splitting, machine learning, model development, feature extraction, and model evaluation were done. An independent t-test was run on each nuclear characteristic, and Pearson's correlation coefficient test was performed with Statistical Package for the Social Sciences (SPSS) software (IBM SPSS Statistics for Windows, Version 28.0. IBM Corp, Armonk, NY, USA).

RESULTS

 The study found substantial variations between the study and control groups in nuclear size (p<0.05), nuclear shape (p<0.01), and chromatin distribution (p<0.001). The Pearson correlation coefficient of SVM was 0.6472, and CNN was 0.7790, showing that SVM had more accuracy.

CONCLUSION

The availability of multidimensional datasets, combined with breakthroughs in high-performance computers and new deep-learning architectures, has resulted in an explosion of AI use in numerous areas of oncology research. The discerned diagnostic accuracy exhibited by the SVM and CNN models suggests prospective improvements in early detection rates, potentially improving patient outcomes and enhancing healthcare practices.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Expression of PTGS2 along with genes regulating VEGF signalling pathway and association with high-risk factors in locally advanced oral squamous cell carcinoma

BACKGROUND

PTGS2 encodes cyclooxygenase-2 (COX-2), which catalyses the committed step in prostaglandin synthesis. Various in vivo and in vitro data suggest that COX-2 mediates the VEGF signalling pathway. In silico analysis performed in TCGA, PanCancer Atlas for head and neck cancers, demonstrated significant expression and co-expression of PTGS2 and genes that regulate VEGF signalling. This study was designed to elucidate the expression pattern of PTGS2 and genes regulating VEGF signalling in patients with locally advanced oral squamous cell carcinoma (OSCC).

METHODOLOGY

Tumour and normal tissue samples were collected from patients with locally advanced OSCC. RNA was isolated from tissue samples, followed by cDNA synthesis. The cDNA was used for gene expression analysis (RT-PCR) using target-specific primers. The results obtained were compared with the in silico gene expression of the target genes in the TCGA datasets. Co-expression analysis was performed to establish an association between PTGS2 and VEGF signalling genes.

RESULTS

Tumour and normal tissue samples were collected from 24 OSCC patients. Significant upregulation of PTGS2 expression was observed. Furthermore, VEGFA, KDR, CXCR1 and CXCR2 were significantly upregulated in tumour samples compared with paired normal samples, except for VEGFB, whose expression was not statistically significant. A similar expression pattern was observed in silico, except for CXCR2 which was highly expressed in the normal samples. Co-expression analysis showed a significant positive correlation between PTGS2 and VEGF signalling genes, except for VEGFB which showed a negative correlation.

CONCLUSION

PTGS2 and VEGF signalling genes are upregulated in OSCC, which has a profound impact on clinical outcomes.

Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs

The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with 'ImageNet' weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a 'normal' class with 2494 images and an 'OSCC' (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis.