A radiomics model based on preoperative gadoxetic acid–enhanced magnetic resonance imaging for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma

BACKGROUND: Post-hepatectomy liver failure (PHLF) is a fatal complication after liver resection in patients with hepatocellular carcinoma (HCC). It is of clinical importance to estimate the risk of PHLF preoperatively. AIMS: This study aimed to develop and validate a prediction model based on preoperative gadoxetic acid–enhanced magnetic resonance imaging to estimate the risk of PHLF in patients with HCC. METHODS: A total of 276 patients were retrospectively included and randomly divided into training and test cohorts (194:82). Clinicopathological variables were assessed to identify significant indicators for PHLF prediction. Radiomics features were extracted from the normal liver parenchyma at the hepatobiliary phase and the reproducible, robust and non-redundant ones were filtered for modeling. Prediction models were developed using clinicopathological variables (Clin-model), radiomics features (Rad-model), and their combination. RESULTS: The PHLF incidence rate was 24% in the whole cohort. The combined model, consisting of albumin–bilirubin (ALBI) score, indocyanine green retention test at 15 min (ICG-R15), and Rad-score (derived from 16 radiomics features) outperformed the Clin-model and the Rad-model. It yielded an area under the receiver operating characteristic curve (AUC) of 0.84 (95% confidence interval (CI): 0.77–0.90) in the training cohort and 0.82 (95% CI: 0.72–0.91) in the test cohort. The model demonstrated a good consistency by the Hosmer–Lemeshow test and the calibration curve. The combined model was visualized as a nomogram for estimating individual risk of PHLF. CONCLUSION: A model combining clinicopathological risk factors and radiomics signature can be applied to identify patients with high risk of PHLF and serve as a decision aid when planning surgery treatment in patients with HCC.