Zhang WY, Chang YJ, Shi RH. Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era. World J Gastroenterol 2024; 30(39): 4267-4280 [DOI: 10.3748/wjg.v30.i39.4267]
Corresponding Author of This Article
Rui-Hua Shi, PhD, Department of Gastroenterology, Zhongda Hospital, Southeast University, No. 87 Dingjiaqiao, Nanjing 210009, Jiangsu Province, China. ruihuashi@126.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Oct 21, 2024; 30(39): 4267-4280 Published online Oct 21, 2024. doi: 10.3748/wjg.v30.i39.4267
Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era
Wan-Yue Zhang, Yong-Jian Chang, Rui-Hua Shi
Wan-Yue Zhang, School of Medicine, Southeast University, Nanjing 221000, Jiangsu Province, China
Yong-Jian Chang, School of Cyber Science and Engineering, Southeast University, Nanjing 210009, Jiangsu Province, China
Rui-Hua Shi, Department of Gastroenterology, Zhongda Hospital, Southeast University, Nanjing 210009, Jiangsu Province, China
Author contributions: Zhang WY contributed to planning and conducting the study, writing - original draft, conceptualization; Chang YJ contributed to writing - review & editing; Shi RH contributed to supervision, conceptualization.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Received: April 28, 2024 Revised: August 31, 2024 Accepted: September 19, 2024 Published online: October 21, 2024 Processing time: 166 Days and 13.1 Hours
Abstract
Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer with a poor prognosis. Early diagnosis and prognosis assessment are crucial for improving the survival rate of ESCC patients. With the advancement of artificial intelligence (AI) technology and the proliferation of medical digital information, AI has demonstrated promising sensitivity and accuracy in assisting precise detection, treatment decision-making, and prognosis assessment of ESCC. It has become a unique opportunity to enhance comprehensive clinical management of ESCC in the era of precision oncology. This review examines how AI is applied to the diagnosis, treatment, and prognosis assessment of ESCC in the era of precision oncology, and analyzes the challenges and potential opportunities that AI faces in clinical translation. Through insights into future prospects, it is hoped that this review will contribute to the real-world application of AI in future clinical settings, ultimately alleviating the disease burden caused by ESCC.
Core Tip: This article provides an overview of the current status of esophageal squamous cell carcinoma (ESCC) diagnosis and treatment, emphasizing the pivotal role of artificial intelligence (AI)-based predictive models in enhancing the precision of ESCC management. It outlines the existing challenges and opportunities associated with the integration of AI technologies in the diagnosis and treatment of ESCC. Furthermore, the article discusses the future research directions necessary to advance the practical application of AI in clinical settings, aiming to improve the accuracy and efficacy of ESCC care.
