Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis

doi:10.1007/s00330-022-09203-6

	Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis
	Zhao, Qin-xian 1; He, Xue-lei 2,3; Wang, Kun3 ; Cheng, Zhi-gang 1; Han, Zhi-yu 1; Liu, Fang-yi 1; Yu, Xiao-ling 1; Hui, Zhong 4; Yu, Jie 1; Chao, An 1; Liang, Ping 1
发表期刊	EUROPEAN RADIOLOGY
ISSN	0938-7994
	2022-11-24
页码	11
通讯作者	Yu, Jie(jiemi301@163.com) ; Chao, An(anchao-1983@163.com) ; Liang, Ping(liangping301@126.com)
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.
关键词	Thermal ablation Colorectal neoplasms Ultrasound Deep learning Recurrence
DOI	10.1007/s00330-022-09203-6
关键词[WOS]	HEPATOCELLULAR-CARCINOMA ; MICROWAVE ABLATION ; RISK-FACTORS ; INTRAHEPATIC RECURRENCE ; RESECTION ; EXPERIENCE ; MANAGEMENT ; EFFICACY ; PATTERNS ; SAFETY
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000886855000004
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/51288
专题	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.Chinese Peoples Liberat Army Gen Hosp, Dept Intervent Ultrasound, Med Ctr 5, 28 Fuxing Rd, Beijing 100853, Peoples R China 2.Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Shaanxi, Peoples R China 3.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 4.Minist Educ, Key Lab Biomed Informat Engn, Xian, 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.