Analysis of Curative Effect and Prognostic Factors of Radiotherapy for Esophageal Cancer Based on the CNN

Abstract

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.

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

“…The largest cluster was “radiomics” (#0) ( 53 – 56 ), while “tumor segmentation”(#6) ( 57 – 61 )was the earliest research in this field. “Endoscopy” (#9) ( 62 – 65 ) and “deep learning” (#2) ( 66 – 70 ) were the latest research hotspots. Figure 7D exhibits the top 25 references with the strongest citation bursts.…”

Systematic review and meta-analysis of endoscopic ultrasonography in staging diagnosis of esophageal cancer after neoadjuvant radiotherapy and chemotherapy

“…Simple surgical treatment cannot meet the clinical needs of patients with esophageal cancer (43,44). To further enhance the longterm survival rate of patients with esophageal cancer, the implementation of neoadjuvant chemotherapy before surgery attracts extensive attention from medical staff and it shows good therapeutic effects (45). Multiple studies show that neoadjuvant chemotherapy can effectively improve the long-term survival rate of patients with esophageal cancer and alleviate patients' clinical symptoms (46,47).…”

Machine Learning and Novel Biomarkers Associated with Immune Infiltration for the Diagnosis of Esophageal Squamous Cell Carcinoma

“…Patients diagnosed with ESCC have a survival rate of fewer than 30 percent after 5 years. Despite the fact that various biomarkers for ESCC have been discovered, the therapeutic relevance of the majority of them has not been validated [ 20 , 21 ]. Therefore, there is an immediate need for research into more effective biomarkers for the diagnosis of ESCC.…”

“…Several types of cancers are affected by MMPs in different ways [24,25]. As of this writing, a total of 24 MMPs have been discovered (MMP1, 2, 3, 4, 5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,23, 23a/23b, 24, 25, 26, 27, and 28). Liu et al reported that lymph node metastases, microvessel density, and an advanced TNM stage were all linked to ESCC patients with elevated MMP1 expression.…”