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  4. 中国科学院分子影像重点实验室
Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis

文献类型：期刊论文

作者Zhao, Qin-xian4; He, Xue-lei2,3; Wang, Kun2; Cheng, Zhi-gang4; Han, Zhi-yu4; Liu, Fang-yi4; Yu, Xiao-ling4; Hui, Zhong1; Yu, Jie4; Chao, An4
刊名EUROPEAN RADIOLOGY
出版日期2022-11-24
页码11
关键词Thermal ablation Colorectal neoplasms Ultrasound Deep learning Recurrence
ISSN号0938-7994
DOI10.1007/s00330-022-09203-6
通讯作者Yu, Jie(jiemi301@163.com) ; Chao, An(anchao-1983@163.com) ; Liang, Ping(liangping301@126.com)
英文摘要Objectives To develop and validate a deep learning (DL) model based on quantitative analysis of contrast-enhanced ultrasound (CEUS) images that predicts early recurrence (ER) after thermal ablation (TA) of colorectal cancer liver metastasis (CRLM). Methods Between January 2010 and May 2019, a total of 207 consecutive patients with CRLM with 13,248 slice images at three dynamic phases who received CEUS within 2 weeks before TA were retrospectively enrolled in two centres (153 for the training cohort (TC), 32 for the internal test cohort (ITC), and 22 for the external test cohort (ETC)). Clinical and CEUS data were used to develop and validate the clinical model, DL model, and DL combining with clinical (DL-C) model to predict ER after TA. The performance of these models was compared by the receiver operating characteristic curve (ROC) with the DeLong test. Results After a median follow-up of 56 months, 49% (99/207) of patients experienced ER. Three key clinical features (preoperative chemotherapy (PC), lymph node metastasis of the primary colorectal cancer (LMPCC), and T stage) were used to develop the clinical model. The DL model yielded better performance than the clinical model in the ETC (AUC: 0.67 for the clinical model, 0.76 for the DL model). The DL-C model significantly outperformed the clinical model and DL model (AUC: 0.78 for the DL-C model in the ETC; both, p < 0.001). Conclusions The model based on CEUS can achieve satisfactory prediction and assist physicians during the therapeutic decision-making process in clinical practice.
WOS关键词HEPATOCELLULAR-CARCINOMA ; MICROWAVE ABLATION ; RISK-FACTORS ; INTRAHEPATIC RECURRENCE ; RESECTION ; EXPERIENCE ; MANAGEMENT ; EFFICACY ; PATTERNS ; SAFETY
资助项目National Natural Science Foundation of China[81971625,91859201] ; National Natural Science Foundation of China[82102076] ; National Natural Science Foundation of China[82030047]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000886855000004
出版者SPRINGER
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/51287]  
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Yu, Jie; Chao, An; Liang, Ping
作者单位1.Minist Educ, Key Lab Biomed Informat Engn, Xian, Peoples R China
2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
3.Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Shaanxi, Peoples R China
4.Chinese Peoples Liberat Army Gen Hosp, Dept Intervent Ultrasound, Med Ctr 5, 28 Fuxing Rd, Beijing 100853, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Qin-xian,He, Xue-lei,Wang, Kun,et al. Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis[J]. EUROPEAN RADIOLOGY,2022:11.
APA Zhao, Qin-xian.,He, Xue-lei.,Wang, Kun.,Cheng, Zhi-gang.,Han, Zhi-yu.,...&Liang, Ping.(2022).Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis.EUROPEAN RADIOLOGY,11.
MLA Zhao, Qin-xian,et al."Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis".EUROPEAN RADIOLOGY (2022):11.

入库方式： OAI收割

来源：自动化研究所

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