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

doi:10.1007/s00330-019-06553-6

	Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound
	Liu, Dan 1,2; Liu, Fei2,3 ; Xie, Xiaoyan 1; Su, Liya 1; Liu, Ming 1; Xie, Xiaohua 1; Kuang, Ming 1,4; Huang, Guangliang 1; Wang, Yuqi2,3 ; Zhou, Hui2,3 ; Wang, Kun2,3 ; Lin, Manxia 1; Tian, Jie 2,5
发表期刊	EUROPEAN RADIOLOGY
ISSN	0938-7994
	2020-04-01
卷号	30 期号:4 页码:2365-2376
通讯作者	Lin, Manxia(linmxia@mail.sysu.edu.cn) ; Tian, Jie(tian@ieee.org)
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.
关键词	Therapeutic chemoembolization Hepatocellular carcinoma Ultrasonography Deep learning
DOI	10.1007/s00330-019-06553-6
关键词[WOS]	TRANSCATHETER ARTERIAL CHEMOEMBOLIZATION ; EVALUATION CRITERIA ; MODIFIED RECIST ; SOLID TUMORS ; SYSTEM ; EMBOLIZATION ; SORAFENIB
收录类别	SCI
语种	英语
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000519659200056
出版者	SPRINGER
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：79[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38598
专题	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.Sun Yat Sen Univ, Affiliated Hosp 1, Inst Diagnost & Intervent Ultrasound, Dept Med Ultrason, Guangzhou 510080, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Dept Artificial Intelligence Technol, 19 A Yuquan Rd, Beijing 100049, Peoples R China 4.Sun Yat Sen Univ, Affiliated Hosp 1, Dept Liver Surg, Guangzhou 510080, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.