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Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma

文献类型：期刊论文

作者Xu, Wenxin1; Zhang, Haoyan2,3; Zhang, Rui1; Zhong, Xian1; Li, Xiaoju1; Zhou, Wenwen1; Xie, Xiaoyan1; Wang, Kun2,3; Xu, Ming1
刊名EUROPEAN RADIOLOGY
出版日期2024-07-27
页码12
关键词Vessels encapsulating tumor clusters Contrast-enhanced ultrasound Deep learning Prediction
ISSN号0938-7994
DOI10.1007/s00330-024-10985-0
通讯作者Xu, Ming(xuming8@mail.sysu.edu.cn)
英文摘要ObjectivesTo establish and validate a non-invasive deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict vessels encapsulating tumor clusters (VETC) patterns in hepatocellular carcinoma (HCC).Materials and methodsThis retrospective study included consecutive HCC patients with preoperative CEUS images and available tissue specimens. Patients were randomly allocated into the training and test cohorts. CEUS images were analyzed using the ResNet-18 convolutional neural network for the development and validation of the VETC predictive model. The predictive value for postoperative early recurrence (ER) of the proposed model was further evaluated.ResultsA total of 242 patients were enrolled finally, including 195 in the training cohort (54.6 +/- 11.2 years, 178 males) and 47 in the test cohort (55.1 +/- 10.6 years, 40 males). The DL model (DL signature) achieved favorable performance in both the training cohort (area under the receiver operating characteristics curve [AUC]: 0.92, 95% confidence interval [CI]: 0.88-0.96) and test cohort (AUC: 0.90, 95% CI: 0.82-0.99). The stratified analysis demonstrated good discrimination of DL signature regardless of tumor size. Moreover, the DL signature was found independently correlated with postoperative ER (hazard ratio [HR]: 1.99, 95% CI: 1.29-3.06, p = 0.002). C-indexes of 0.70 and 0.73 were achieved when the DL signature was used to predict ER independently and combined with clinical features.ConclusionThe proposed DL signature provides a non-invasive and practical method for VETC-HCC prediction, and contributes to the identification of patients with high risk of postoperative ER.Clinical relevance statementThis DL model based on contrast-enhanced US displayed an important role in non-invasive diagnosis and prognostication for patients with VETC-HCC, which was helpful in individualized management.Key Points...
WOS关键词PATTERN
资助项目National Natural Science Foundation of China[92059201] ; National Natural Science Foundation of China[82071951] ; National Natural Science Foundation of China[82102057] ; National Natural Science Foundation of China[92159305] ; National Natural Science Foundation of China[92259303] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[82272029] ; Guangdong Basic and Applied Basic Research Foundation[2020A1515111055] ; Beijing Science Fund for Distinguished Young Scholars[JQ22013] ; National Key Research and Development Program of China[2022YFC2407405] ; Excellent Member Project of the Youth Innovation Promotion Association CAS[2016124]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:001279122800001
出版者SPRINGER
资助机构National Natural Science Foundation of China ; Guangdong Basic and Applied Basic Research Foundation ; Beijing Science Fund for Distinguished Young Scholars ; National Key Research and Development Program of China ; Excellent Member Project of the Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/59364]  
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Xu, Ming
作者单位1.Sun Yat Sen Univ, Affiliated Hosp 1, Inst Diagnost & Intervent Ultrasound, Dept Med Ultrason, Guangzhou, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Wenxin,Zhang, Haoyan,Zhang, Rui,et al. Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma[J]. EUROPEAN RADIOLOGY,2024:12.
APA Xu, Wenxin.,Zhang, Haoyan.,Zhang, Rui.,Zhong, Xian.,Li, Xiaoju.,...&Xu, Ming.(2024).Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.EUROPEAN RADIOLOGY,12.
MLA Xu, Wenxin,et al."Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma".EUROPEAN RADIOLOGY (2024):12.

入库方式： OAI收割

来源：自动化研究所

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