Published online Mar 21, 2020. doi: 10.3748/wjg.v26.i11.1208
Peer-review started: October 12, 2019
First decision: January 13, 2020
Revised: February 18, 2020
Accepted: February 21, 2020
Article in press: February 21, 2020
Published online: March 21, 2020
Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma (HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.
To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.
For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.
Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure (area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure (area under the curve: 0.894; 95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model (integrated discrimination improvement = 0.117, P = 0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic (P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.
A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy.
Core tip: Serological indexes, indocyanine green clearance rate at 15 min, liver volumetry, and clinical scoring systems are commonly used to determine liver function capacity and predict postoperative residual liver function. However, these indexes are not sufficiently accurate for predicting the risk of postoperative liver failure. We constructed a radiomics signature based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance imaging. This radiomics signature achieves favorable performance in predicting liver failure in cirrhotic patients with hepatocellular carcinoma after major hepatectomy. Incorporating indocyanine green clearance rate at 15 min into the radiomics signature further improves the predictive performance for postoperative liver failure.