Review Article
Image-based artificial intelligence predicts the efficacy of neoadjuvant chemoradiotherapy for esophageal cancer
Liu Yunsong, Ma Zeliang, Men Yu, Hui Zhouguang
Published 2024-11-15
Cite as Chin J Radiat Oncol, 2024, 33(11): 1070-1076. DOI: 10.3760/cma.j.cn113030-20240312-00095
Abstract
Neoadjuvant chemoradiotherapy combined with surgery is the standard treatment for patients with locally advanced esophageal cancer. However, there is significant variability in how patients respond to neoadjuvant chemoradiotherapy. The value of existing conventional diagnostic methods in predicting the effectiveness of neoadjuvant chemoradiotherapy is limited. Image-based artificial intelligence (AI), particularly radiomics and deep learning technologies, have shown great potential in predicting the efficacy of neoadjuvant chemoradiotherapy by automatically quantifying and analyzing a vast amount of information in medical images. This review summarizes AI research based on CT, positron emission computed tomography (PET-CT), and other imaging modalities, highlighting the current limitations and future directions of the research.
Key words:
Esophageal neoplasms; Medical imaging; Artificial intelligence; Neoadjuvant chemoradiotherapy
Contributor Information
Liu Yunsong
Department of Radiation Oncology, National Cancer Center / National Clinical Research Center for Cancer / Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Ma Zeliang
Department of Radiation Oncology, National Cancer Center / National Clinical Research Center for Cancer / Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Men Yu
Department of VIP Medical Services, National Cancer Center / National Clinical Research Center for Cancer / Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Hui Zhouguang
Department of VIP Medical Services, National Cancer Center / National Clinical Research Center for Cancer / Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China