In World journal of gastroenterology ; h5-index 103.0
Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.
Wong Pak Kin, Chan In Neng, Yan Hao-Ming, Gao Shan, Wong Chi Hong, Yan Tao, Yao Liang, Hu Ying, Wang Zhong-Ren, Yu Hon Ho
2022-Dec-07
Deep learning, Gastrointestinal cancer, Medical imaging, Radiomics
