Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma - Sun, Zhongqi; Shi, Zhongxing; Xin, Yanjie; Zhao, Sheng; Jiang, Hao; Wang, Dandan; Zhang, Linhan; Wang, Ziao; Dai, Yanmei; Jiang, Huijie (Frontiers in Bioengineering and Biotechnology, 2021)
Hepatocellular carcinoma (HCC) ranks the second most lethal tumor globally and is the fourth leading cause of cancer-related death worldwide. Unfortunately, HCC is commonly at intermediate tumor stage or advanced tumor stage, which only some palliative treatment can be used to offer a limited overall survival. Due to highly heterogeneous of the genetic, molecular and histological levels, HCC makes the prediction of preoperative transarterial chemoembolization (TACE) efficacy and the development of personalized regimens challenging. In this study, a new multi-modal point-of-care system was employed to predict the response of TACE in HCC by a concept of integrating multi-modal large-scale data of clinical index and (computerized tomography) CT images. This multi-modal point-of-care predicting system opens new possibilities for predicting the response of TACE treatment and can help clinicians to select the optimal patients with HCC who can benefit from the interventional therapy.