A Clinical-Radiomic Model for Predicting Indocyanine Green Retention Rate at 15 Min in Patients With Hepatocellular Carcinoma - Wu, Ji; Xie, Feng; Ji, Hao; Zhang, Yiyang; Luo, Yi; Xia, Lei; Lu, Tianfei; He, Kang; Sha, Meng; Zheng, Zhigang; Yong, Junekong; Li, Xinming; et al. (Frontiers in Surgery, 2022)
Purpose: The indocyanine green retention rate at 15 min (ICG-R15) is of great importance in accurate assessment of hepatic functional reserve for safe hepatic resection. To assist clinicians to evaluate hepatic functional reserve in medical institutions that lack the 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. 14 variables significantly associated with ICG-R15 were picked out for model development. Compared to the other two models, XGBR achieved the best performance in predicting ICG-R15, with a mean difference of 1.59% (median, 1.53%) and R value of 0.90. Delong test result showed no significant difference in the AUROCs for predicting posthepatectomy liver failure between actual and estimated ICG-R15. Conclusion: The proposed approach that incorporates the optimal radiomics features and clinical factors can allow for individualized prediction of ICG-R15 value of HCC patients, regardless of the specific equipment and detection reagent (NO. ChiCTR2100053042; URL, http://www.chictr.org.cn).