CASIA OpenIR
Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound
Liu, Dan1,2; Liu, Fei2,3; Xie, Xiaoyan1; Su, Liya1; Liu, Ming1; Xie, Xiaohua1; Kuang, Ming1,4; Huang, Guangliang1; Wang, Yuqi2,3; Zhou, Hui2,3; Wang, Kun2,3; Lin, Manxia1; Tian, Jie2,5
Source PublicationEUROPEAN RADIOLOGY
ISSN0938-7994
2020-04-01
Volume30Issue:4Pages:2365-2376
Corresponding AuthorLin, Manxia(linmxia@mail.sysu.edu.cn) ; Tian, Jie(tian@ieee.org)
AbstractObjectives 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.
KeywordTherapeutic chemoembolization Hepatocellular carcinoma Ultrasonography Deep learning
DOI10.1007/s00330-019-06553-6
WOS KeywordTRANSCATHETER ARTERIAL CHEMOEMBOLIZATION ; EVALUATION CRITERIA ; MODIFIED RECIST ; SOLID TUMORS ; SYSTEM ; EMBOLIZATION ; SORAFENIB
Indexed BySCI
Language英语
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000519659200056
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38598
Collection中国科学院自动化研究所
Corresponding AuthorLin, Manxia; Tian, Jie
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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