No abstract

Epidermal growth factor receptor (EGFR) signaling is an important pathway that is not only involved in the determination of cellular development, but also has significant roles in tumor development and progression. The study aims to examine the expression of EGFR signaling pathway-related proteins (EGFR, c-Fos, and c-erb-B2) in esophageal squamous cell carcinoma and to investigate their relationships with clinical significance. Sixty esophageal squamous carcinoma specimens obtained by fiber esophagoscope were subjected to two-step immunohistochemistry to test the expression of EGFR, c-Fos, and c-erb-B2. EGFR expression was observed in 73.3% of tumors (44/60); positive EGFR expression was significantly correlated with tumor-node-metastasis (TNM) staging, lymph node metastasis, and distant metastasis (P < 0.05). c-Fos expression was found in 85% (51/60) of tumors, and its expression was significantly related to tumor depth and TNM staging (P < 0.05). c-erb-B2 expression was 75% (45/60) in esophageal squamous cell carcinoma (ESCC) specimens, and positive-erb-B2 expression had a significant association with the depth of tumor invasion (P < 0.05). c-Fos expression was significantly and positively correlated with c-erb-B2 (P < 0.05). Overexpression of EGFR, c-Fos, and c-erb-B2 was associated with tumor progression and development. EGFR and c-Fos expression can predict the tumor stage. c-Fos and c-erb-B2 expression can be used to determine the depth of tumor invasion and can also act as a combined prognostic indicator in ESCC.

An esophageal cancer intelligent diagnosis system is developed to improve the recognition rate of esophageal cancer image diagnosis and the efficiency of physicians, as well as to improve the level of esophageal cancer image diagnosis in primary care institutions. In this paper, by collecting medical images related to esophageal cancer over the years, we establish an intelligent diagnosis system based on the convolutional neural network for esophageal cancer images through the steps of data annotation, image preprocessing, data enhancement, and deep learning to assist doctors in intelligent diagnosis. The convolutional neural network-based esophageal cancer image intelligent diagnosis system has been successfully applied in hospitals and widely praised by frontline doctors. This system is beneficial for primary care physicians to improve the overall accuracy of esophageal cancer diagnosis and reduce the risk of death of esophageal cancer patients. We also analyze that the efficacy of radiation therapy for esophageal cancer can be influenced by many factors, and clinical attention should be paid to grasp the relevant factors in order to improve the final treatment effect and prognosis of patients.

No abstract

No abstract