Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma
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 ove...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2021.761548/full |
| _version_ | 1818932376112201728 |
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| author | Zhongqi Sun Zhongxing Shi Yanjie Xin Sheng Zhao Hao Jiang Dandan Wang Linhan Zhang Ziao Wang Yanmei Dai Huijie Jiang |
| author_facet | Zhongqi Sun Zhongxing Shi Yanjie Xin Sheng Zhao Hao Jiang Dandan Wang Linhan Zhang Ziao Wang Yanmei Dai Huijie Jiang |
| author_sort | Zhongqi Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T04:31:30Z |
| format | Article |
| id | doaj.art-9516f60b22bc43a3957c05beb7155669 |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-12-20T04:31:30Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-9516f60b22bc43a3957c05beb71556692022-12-21T19:53:22ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852021-11-01910.3389/fbioe.2021.761548761548Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular CarcinomaZhongqi SunZhongxing ShiYanjie XinSheng ZhaoHao JiangDandan WangLinhan ZhangZiao WangYanmei DaiHuijie JiangHepatocellular 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.https://www.frontiersin.org/articles/10.3389/fbioe.2021.761548/fullhepatocellular carcinomaartificial intelligencecomputed tomography imaginginflammation-based indexpoint-of-care predicting |
| spellingShingle | Zhongqi Sun Zhongxing Shi Yanjie Xin Sheng Zhao Hao Jiang Dandan Wang Linhan Zhang Ziao Wang Yanmei Dai Huijie Jiang Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma Frontiers in Bioengineering and Biotechnology hepatocellular carcinoma artificial intelligence computed tomography imaging inflammation-based index point-of-care predicting |
| title | Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma |
| title_full | Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma |
| title_fullStr | Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma |
| title_full_unstemmed | Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma |
| title_short | Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma |
| title_sort | artificial intelligent multi modal point of care system for predicting response of transarterial chemoembolization in hepatocellular carcinoma |
| topic | hepatocellular carcinoma artificial intelligence computed tomography imaging inflammation-based index point-of-care predicting |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2021.761548/full |
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