Li Changfeng, Wang Qiang, Zou Mengda, Cai Ping, Li Xuesong, Feng Kai, Zhang Leida, Sparrelid Ernesto, Brismar Torkel B, Ma Kuansheng
Institute of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing, China.
Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden.
Front Oncol. 2023 Jul 5;13:1164739. doi: 10.3389/fonc.2023.1164739. eCollection 2023.
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.
肝切除术后肝衰竭（PHLF）是肝细胞癌（HCC）患者肝切除术后的一种致命并发症。术前评估PHLF风险具有重要临床意义。
本研究旨在开发并验证一种基于术前钆塞酸二钠增强磁共振成像的预测模型，以评估HCC患者发生PHLF的风险。
回顾性纳入276例患者，并随机分为训练队列和测试队列（194:82）。评估临床病理变量以确定预测PHLF的显著指标。从肝胆期正常肝实质中提取影像组学特征，并筛选出可重复、稳健且非冗余的特征用于建模。使用临床病理变量（临床模型）、影像组学特征（影像模型）及其组合开发预测模型。
整个队列中PHLF发生率为24%。由白蛋白-胆红素（ALBI）评分、15分钟吲哚菁绿滞留率（ICG-R15）和影像评分（源自16个影像组学特征）组成的联合模型优于临床模型和影像模型。在训练队列中，其受试者工作特征曲线下面积（AUC）为0.84（95%置信区间（CI）：0.77 - 0.90），在测试队列中为0.82（95%CI：0.72 - 0.91）。该模型经Hosmer-Lemeshow检验和校准曲线显示具有良好的一致性。联合模型可直观化为用于估计个体PHLF风险的列线图。
结合临床病理风险因素和影像组学特征的模型可用于识别PHLF高风险患者，并在规划HCC患者手术治疗时作为决策辅助工具。
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