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

Frontiers in Surgery

article

Yi Luo¹  Lei Xia¹  Tianfei Lu¹  Kang He¹  Meng Sha¹  Zhigang Zheng¹  Junekong Yong¹  Xinming Li²  Di Zhao³  Yuting Yang⁴  Qiang Xia¹  Feng Xue¹  Ji Wu¹  Feng Xie⁴  Hao Ji¹  Yiyang Zhang⁴ 

[1] Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University;Department of Medical Imaging, Zhujiang Hospital, Southern Medical University;Institute of Computing Technology, Chinese Academy of Sciences;Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University

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

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Purpose: 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). Methods 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. Results 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.

【 授权许可】

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