Field cancerization revisited in purview of quantum entanglement: Delving into the unexplored

Comparison of Broder's and Bryne's Grading System for Oral Squamous Cell Carcinoma With Lymph Node Metastases and Prognosis: A Scoping Review

Oral squamous cell carcinoma (OSCC) has the highest mortality rate of any type of head and neck squamous cell carcinoma. For many eons, the clinical TNM (tumor size, nodal metastasis, and distant metastasis) classification and histological grading of malignancies have been used to predict clinical behavior, confusing it with prognosis and overall survival. This review aimed to systematically identify and evaluate the prognostic value of Broder's and Bryne's grading system for OSCC. Electronic resources such as PubMed, Cochrane Database of Systematic Reviews, Google Scholar, Scopus, and direct web searches were used to conduct a thorough search. The titles were examined to identify relevant papers, which were then reviewed for inclusion by reading the abstract. To incorporate studies published outside of the electronic database, the bibliography of all recognized papers was scanned. This review examined all research that investigated the prognostic value of Broder's and Bryne's grading systems in OSCC. The electronic database search identified 221 articles. After reading full articles, based on the titles and abstracts and after removing duplicates, six articles were screened. Finally, six articles were selected based on their ability to meet the inclusion criteria and answer the research question. All studies analyzed the competence of this histological grading system in predicting the prognosis of OSCC patients. Four studies evaluated lymph node metastasis and two studies analyzed the histological grading of OSCC. While evaluating the histological grade, we recommend the application of Bryne's (1992) system for grading OSCC. The standardization of a single, effective method would make it easier to compare results from various studies. This grading system yields better interobserver agreement and bears a prognostic value which may help in devising a treatment strategy for better patient outcomes.

Application of InceptionV3, SqueezeNet, and VGG16 Convoluted Neural Networks in the Image Classification of Oral Squamous Cell Carcinoma: A Cross-Sectional Study

Background Artificial intelligence (AI) is a rapidly emerging field in medicine and has applications in diagnostics, therapeutics, and prognostication in various malignancies. The present study was conducted to analyze and compare the accuracy of three deep learning neural networks for oral squamous cell carcinoma (OSCC) images. Materials and methods Three hundred and twenty-five cases of OSCC were included and graded histologically by two grading systems. The images were then analyzed using the Orange data mining tool. Three neural networks, viz., InceptionV3, SqueezeNet, and VGG16, were used for further analysis and classification. Positive predictive value, negative predictive value, specificity, sensitivity, area under curve (AUC), and accuracy were estimated for each neural network. Results Histological grading by Bryne's yielded significantly stronger inter-observer agreement. The highest accuracy was found for the classification of poorly differentiated squamous cell carcinoma images irrespective of the network used. Other values were variegated. Conclusion AI could serve as an adjunct for improvement in theragnostics. Further research is required to achieve the modification of mining tools for greater predictive values, sensitivity, specificity, AUC, accuracy, and security. Bryne's grading system is warranted for the better application of AI in OSCC image analytics.