Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma
Autor: | Yanmei Dai, Ziao Wang, Shi Zhongxing, Hao Jiang, Sheng Zhao, Dandan Wang, Yanjie Xin, Linhan Zhang, Zhongqi Sun, Jiang Huijie |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Interventional therapy
medicine.medical_specialty Histology inflammation-based index Palliative treatment business.industry Biomedical Engineering Bioengineering and Biotechnology Bioengineering hepatocellular carcinoma point-of-care predicting medicine.disease artificial intelligence digestive system diseases Hepatocellular carcinoma Tumor stage Overall survival Medicine Radiology computed tomography imaging business TP248.13-248.65 Point of care Original Research Biotechnology |
Zdroj: | Frontiers in Bioengineering and Biotechnology, Vol 9 (2021) Frontiers in Bioengineering and Biotechnology |
ISSN: | 2296-4185 |
DOI: | 10.3389/fbioe.2021.761548/full |
Popis: | 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. |
Databáze: | OpenAIRE |
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