Identification of prognostic immune genes in laryngeal cancer

An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images

Recently, laryngeal cancer cases have increased drastically across the globe. Accurate treatment for laryngeal cancer is intricate, especially in the later stages. This type of cancer is an intricate malignancy inside the head and neck area of patients. In recent years, diverse diagnosis approaches and tools have been developed by researchers for helping clinical experts to identify laryngeal cancer effectively. However, these existing tools and approaches have diverse issues related to performance constraints such as lower accuracy in the identification of laryngeal cancer in the initial stage, more computational complexity, and large time consumption in patient screening. In this paper, the authors present a novel and enhanced deep-learning-based Mask R-CNN model for the identification of laryngeal cancer and its related symptoms by utilizing diverse image datasets and CT images in real time. Furthermore, our suggested model is capable of capturing and detecting minor malignancies of the larynx portion in a significant and faster manner in the real-time screening of patients, and it saves time for the clinicians, allowing for more patient screening every day. The outcome of the suggested model is enhanced and pragmatic and obtained an accuracy of 98.99%, precision of 98.99%, F1 score of 97.99%, and recall of 96.79% on the ImageNet dataset. Several studies have been performed in recent years on laryngeal cancer detection by using diverse approaches from researchers. For the future, there are vigorous opportunities for further research to investigate new approaches for laryngeal cancer detection by utilizing diverse and large dataset images.

DIAPH2, PTPRD and HIC1 Gene Polymorphisms and Laryngeal Cancer Risk

AIM, DIAPH2, PTPRD and HIC1 are the cell glycoprotein, which play an important role in the occurrence and development of tumors. This study was designed to assess the association between DIAPH2, PTPRD and HIC1 SNPs and laryngeal cancer risk. PATIENTS AND METHODS: This study including 267 patients with histologically confirmed laryngeal cancer and 157 controls. The relationship between genetic variations DIAPH2 (rs6620138), PTPRD (rs3765142) and HIC1 (rs9901806) and the onset of laryngeal cancer were investigated. Statistical analysis to calculate the relationship between DIAPH2, PTPRD and HIC1 genes polymorphism and pathogenesis of laryngeal cancer. RESULTS: The results showed that rs6620138 DIAPH2 polymorphism could increase the onset risk of laryngeal cancer. Statistically significant differences in allele distribution of rs6620138 DIAPH2 and rs9901806 HIC1 in the case and control groups subgroups. CONCLUSIONS: This study results suggested that genetic variation of rs6620138 DIAPH2 polymorphism is related to the susceptibility to laryngeal cancer. Our results provide a basis to begin basic research on the role of DIAPH2 gene in the pathogenesis of laryngeal cancer.