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

文献类型：期刊论文

作者Wu, Ji2; Xie, Feng3; Ji, Hao2; Zhang, Yiyang3; Luo, Yi2; Xia, Lei2; Lu, Tianfei2; He, Kang2; Sha, Meng2; Zheng, Zhigang2
刊名FRONTIERS IN SURGERY
出版日期2022-03-24
卷号9页码:9
ISSN号2296-875X
关键词indocyanine green retention rate at 15 min radiomics machine learning post hepatectomy liver failure hepatocellular carcinoma
DOI10.3389/fsurg.2022.857838
英文摘要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, ).
资助项目National Science and Technology major projects[2018ZX10723-203]
WOS研究方向Surgery
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000788142000001
源URL[http://119.78.100.204/handle/2XEOYT63/18878]  
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.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
2.Shanghai Jiao Tong Univ, Renji Hosp, Sch Med, Dept Liver Surg, Shanghai, Peoples R China
3.Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Instrument Sci & Engn, Shanghai, Peoples R China
4.Southern Med Univ, Zhujiang Hosp, Dept Med Imaging, Guangzhou, 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.

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来源：计算技术研究所

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