A Clinical-Radiomic Model for Predicting Indocyanine Green Retention Rate at 15 Min in Patients With Hepatocellular Carcinoma

doi:10.3389/fsurg.2022.857838

	A Clinical-Radiomic Model for Predicting Indocyanine Green Retention Rate at 15 Min in Patients With Hepatocellular Carcinoma
	Wu, Ji 1; Xie, Feng 2; Ji, Hao 1; Zhang, Yiyang 2; Luo, Yi 1; Xia, Lei 1; Lu, Tianfei 1; He, Kang 1; Sha, Meng 1; Zheng, Zhigang 1; Yong, Junekong 1; Li, Xinming 3; Zhao, Di 4; Yang, Yuting 2; Xia, Qiang 1; Xue, Feng 1
	2022-03-24
发表期刊	FRONTIERS IN SURGERY
ISSN	2296-875X
卷号	9 页码:9
摘要	Purpose:& nbsp;The indocyanine green retention rate at 15 min (ICG-R15) is of great importance in the accurate assessment of hepatic functional reserve for safe hepatic resection. To assist clinicians to evaluate hepatic functional reserve in medical institutions that lack expensive equipment, we aimed to explore a novel approach to predict ICG-R15 based on CT images and clinical data in patients with hepatocellular carcinoma (HCC).& nbsp;Methods:& nbsp;In this retrospective study, 350 eligible patients were enrolled and randomly assigned to the training cohort (245 patients) and test cohort (105 patients). Radiomics features and clinical factors were analyzed to pick out the key variables, and based on which, we developed the random forest regression, extreme gradient boosting regression (XGBR), and artificial neural network models for predicting ICG-R15, respectively. Pearson's correlation coefficient (R) was adopted to evaluate the performance of the models.& nbsp;Results:& nbsp;We extracted 660 CT image features in total from each patient. Fourteen variables significantly associated with ICG-R15 were picked out for model development. Compared to the other two models, the XGBR achieved the best performance in predicting ICG-R15, with a mean difference of 1.59% (median, 1.53%) and an R-value of 0.90. Delong test result showed no significant difference in the area under the receiver operating characteristic (AUROCs) for predicting post hepatectomy liver failure between actual and estimated ICG-R15.& nbsp;Conclusion:& nbsp;The proposed approach that incorporates the optimal radiomics features and clinical factors can allow for individualized prediction of ICG-R15 value of patients with HCC, regardless of the specific equipment and detection reagent (NO. ChiCTR2100053042; URL, ).
关键词	indocyanine green retention rate at 15 min radiomics machine learning post hepatectomy liver failure hepatocellular carcinoma
DOI	10.3389/fsurg.2022.857838
收录类别	SCI
语种	英语
资助项目	National Science and Technology major projects[2018ZX10723-203]
WOS研究方向	Surgery
WOS类目	Surgery
WOS记录号	WOS:000788142000001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18879
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yang, Yuting; Xue, Feng
作者单位	1.Shanghai Jiao Tong Univ, Renji Hosp, Sch Med, Dept Liver Surg, Shanghai, Peoples R China 2.Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Instrument Sci & Engn, Shanghai, Peoples R China 3.Southern Med Univ, Zhujiang Hosp, Dept Med Imaging, Guangzhou, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wu, Ji,Xie, Feng,Ji, Hao,et al. A Clinical-Radiomic Model for Predicting Indocyanine Green Retention Rate at 15 Min in Patients With Hepatocellular Carcinoma[J]. FRONTIERS IN SURGERY,2022,9:9.
APA	Wu, Ji.,Xie, Feng.,Ji, Hao.,Zhang, Yiyang.,Luo, Yi.,...&Xue, Feng.(2022).A Clinical-Radiomic Model for Predicting Indocyanine Green Retention Rate at 15 Min in Patients With Hepatocellular Carcinoma.FRONTIERS IN SURGERY,9,9.
MLA	Wu, Ji,et al."A Clinical-Radiomic Model for Predicting Indocyanine Green Retention Rate at 15 Min in Patients With Hepatocellular Carcinoma".FRONTIERS IN SURGERY 9(2022):9.