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

• Published in: Frontiers in oncology Vol. 13; p. 1164739
• Main Authors: Li, Changfeng, Wang, Qiang, Zou, Mengda, Cai, Ping, Li, Xuesong, Feng, Kai, Zhang, Leida, Sparrelid, Ernesto, Brismar, Torkel B, Ma, Kuansheng
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 2023
• Subjects: hepatectomy; hepatocellular carcinoma; liver failure; magnetic resonance imaging; Medicin och hälsovetenskap; Oncology; radiomics; hepatectomy; radiomics; liver failure; magnetic resonance imaging; hepatocellular carcinoma
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2023.1164739

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. 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. 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. 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. 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.