Huang Xiaoying, Qin Minghao, Fang Mengjie, Wang Zipei, Hu Chaoen, Zhao Tongyu, Qin Zhuyuan, Zhu Haishan, Wu Ling, Yu Guowei, De Cobelli Francesco, Xie Xuebin, Palumbo Diego, Tian Jie, Dong Di
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
J Natl Cancer Cent. 2024 Dec 27;5(2):113-131. doi: 10.1016/j.jncc.2024.12.006. eCollection 2025 Apr.
Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers. In recent years, an increasing number of studies suggest that artificial intelligence (AI) technology can effectively address clinical tasks related to upper gastrointestinal cancers. These studies mainly focus on four aspects: screening, diagnosis, treatment, and prognosis. In this review, we focus on the application of AI technology in clinical tasks related to upper gastrointestinal cancers. Firstly, the basic application pipelines of radiomics and deep learning in medical image analysis were introduced. Furthermore, we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric cancers. Finally, the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized, and explorations were conducted on the selection of AI algorithms in various scenarios, the popularization of early screening, the clinical applications of AI, and large multimodal models.
上消化道癌主要包括食管癌和胃癌,是全球最常见的癌症之一。每年有许多新的上消化道癌病例,且生存率往往较低。因此,对上消化道癌患者而言,及时筛查、精准诊断、合适的治疗策略及有效的预后评估至关重要。近年来,越来越多的研究表明,人工智能(AI)技术能够有效解决与上消化道癌相关的临床任务。这些研究主要集中在四个方面:筛查、诊断、治疗和预后。在本综述中,我们重点关注AI技术在上消化道癌相关临床任务中的应用。首先,介绍了放射组学和深度学习在医学图像分析中的基本应用流程。此外,我们分别综述了AI技术在食管癌和胃癌上述方面的应用。最后,总结了上消化道癌领域目前面临的局限性和挑战,并对各种场景下AI算法的选择、早期筛查的推广、AI的临床应用及大型多模态模型进行了探讨。