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Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

文献类型：期刊论文

作者Liu, Dan4,5; Liu, Fei1,5; Xie, Xiaoyan4; Su, Liya4; Liu, Ming4; Xie, Xiaohua4; Kuang, Ming2,4; Huang, Guangliang4; Wang, Yuqi1,5; Zhou, Hui1,5
刊名EUROPEAN RADIOLOGY
出版日期2020-04-01
卷号30期号:4页码:2365-2376
关键词Therapeutic chemoembolization Hepatocellular carcinoma Ultrasonography Deep learning
ISSN号0938-7994
DOI10.1007/s00330-019-06553-6
通讯作者Lin, Manxia(linmxia@mail.sysu.edu.cn) ; Tian, Jie(tian@ieee.org)
英文摘要Objectives We aimed to establish and validate an artificial intelligence-based radiomics strategy for predicting personalized responses of hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) session by quantitatively analyzing contrast-enhanced ultrasound (CEUS) cines. Methods One hundred and thirty HCC patients (89 for training, 41 for validation), who received ultrasound examination (CEUS and B-mode) within 1 week before the first TACE session, were retrospectively enrolled. Ultrasonographic data was used for building and validating deep learning radiomics-based CEUS model (R-DLCEUS), machine learning radiomics-based time-intensity curve of CEUS model (R-TIC), and machine learning radiomics-based B-Mode images model (R-BMode), respectively, to predict responses (objective-response and non-response) to TACE with reference to modified response evaluation criteria in solid tumor. The performance of models was compared by areas under the receiver operating characteristic curve (AUC) and the DeLong test was used to compare different AUCs. The prediction robustness was assessed for each model. Results AUCs of R-DLCEUS, R-TIC, and R-BMode were 0.93 (95% CI, 0.80-0.98), 0.80 (95% CI, 0.64-0.90), and 0.81 (95% CI, 0.67-0.95) in the validation cohort, respectively. AUC of R-DLCEUS shows significant difference compared with that of R-TIC (p = 0.034) and R-BMode (p = 0.039), whereas R-TIC was not significantly different from R-BMode. The performance was highly reproducible with different training and validation cohorts. Conclusions DL-based radiomics method can effectively utilize CEUS cines to achieve accurate and personalized prediction. It is easy to operate and holds good potential for benefiting TACE candidates in clinical practice.
WOS关键词TRANSCATHETER ARTERIAL CHEMOEMBOLIZATION ; EVALUATION CRITERIA ; MODIFIED RECIST ; SOLID TUMORS ; SYSTEM ; EMBOLIZATION ; SORAFENIB
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000519659200056
出版者SPRINGER
源URL[http://ir.ia.ac.cn/handle/173211/38597]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Lin, Manxia; Tian, Jie
作者单位1.Univ Chinese Acad Sci, Dept Artificial Intelligence Technol, 19 A Yuquan Rd, Beijing 100049, Peoples R China
2.Sun Yat Sen Univ, Affiliated Hosp 1, Dept Liver Surg, Guangzhou 510080, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
4.Sun Yat Sen Univ, Affiliated Hosp 1, Inst Diagnost & Intervent Ultrasound, Dept Med Ultrason, Guangzhou 510080, Peoples R China
5.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Dan,Liu, Fei,Xie, Xiaoyan,et al. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound[J]. EUROPEAN RADIOLOGY,2020,30(4):2365-2376.
APA Liu, Dan.,Liu, Fei.,Xie, Xiaoyan.,Su, Liya.,Liu, Ming.,...&Tian, Jie.(2020).Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.EUROPEAN RADIOLOGY,30(4),2365-2376.
MLA Liu, Dan,et al."Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound".EUROPEAN RADIOLOGY 30.4(2020):2365-2376.

入库方式： OAI收割

来源：自动化研究所

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