Sun Zhongqi, Shi Zhongxing, Xin Yanjie, Zhao Sheng, Jiang Hao, Wang Dandan, Zhang Linhan, Wang Ziao, Dai Yanmei, Jiang Huijie
Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Front Bioeng Biotechnol. 2021 Nov 15;9:761548. doi: 10.3389/fbioe.2021.761548. eCollection 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, in which only some palliative treatment can be used to offer a limited overall survival. Due to the high heterogeneity 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 is employed to predict the response of TACE in HCC by a concept of integrating multi-modal large-scale data of clinical index and computed 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 select the optimal patients with HCC who can benefit from the interventional therapy.
肝细胞癌（HCC）是全球第二大致命性肿瘤，也是全球癌症相关死亡的第四大主要原因。不幸的是，HCC通常处于肿瘤中期或晚期，在此阶段只能采用一些姑息治疗来提供有限的总生存期。由于基因、分子和组织学水平的高度异质性，HCC使得术前经动脉化疗栓塞术（TACE）疗效的预测以及个性化治疗方案的制定具有挑战性。在本研究中，采用了一种新的多模式即时护理系统，通过整合临床指标和计算机断层扫描（CT）图像的多模式大规模数据的概念来预测HCC中TACE的反应。这种多模式即时护理预测系统为预测TACE治疗反应开辟了新的可能性，并有助于临床医生选择能够从介入治疗中获益的最佳HCC患者。
