本研究的目的是开发和验证基于二维剪切波弹性成像 (2D-SWE) 的可解释放射组学模型，用于预测肝细胞癌 (HCC) 肝切除患者的症状性肝切除术后肝衰竭 (PHLF)。共有 345 名连续患者入组。在训练期间进行了五倍交叉验证，并在独立测试队列中对模型进行了评估。基于 2D-SWE 图像建立了用于预测症状性 PHLF 的多斑块放射组学模型。将临床特征纳入模型中以训练临床放射组学模型。将放射组学模型和临床放射组学模型与包含临床变量和其他临床预测指标的临床模型进行比较，包括终末期肝病（MELD）评分和白蛋白-胆红素（ALBI）评分模型。 Shapley Additive exPlanations（SHAP）用于放射组学模型的事后可解释性。临床放射组学模型在五重交叉验证中获得了 0.867（95% CI 0.787-0.947）的 AUC，该分数高于临床模型（AUC：0.809；95% CI：0.715-0.902）和放射组学模型（AUC：0.746；95% CI：0.681-0.811）的结果。临床放射组学模型显示测试队列中的 AUC 为 0.822，高于临床模型（AUC：0.684，p = 0.007）、放射组学模型（AUC：0.784，p = 0.415）、MELD 评分（AUC：0.529） ，p < 0.001）和 ALBI 评分（AUC：0.644，p = 0.016）。 SHAP分析表明，一阶放射组学特征，包括一阶最大值64×64、一阶90%百分位数64×64和一阶10%百分位数32×32，是PHLF预测最重要的特征。基于 2D-SWE 和临床变量的可解释临床放射组学模型有助于预测 HCC 中的症状性 PHLF。

The aim of this study was to develop and validate an interpretable radiomics model based on two-dimensional shear wave elastography (2D-SWE) for symptomatic post-hepatectomy liver failure (PHLF) prediction in patients undergoing liver resection for hepatocellular carcinoma (HCC).A total of 345 consecutive patients were enrolled. A five-fold cross-validation was performed during training, and the models were evaluated in the independent test cohort. A multi-patch radiomics model was established based on the 2D-SWE images for predicting symptomatic PHLF. Clinical features were incorporated into the models to train the clinical-radiomics model. The radiomics model and the clinical-radiomics model were compared with the clinical model comprising clinical variables and other clinical predictive indices, including the model for end-stage liver disease (MELD) score and albumin-bilirubin (ALBI) score. Shapley Additive exPlanations (SHAP) was used for post hoc interpretability of the radiomics model.The clinical-radiomics model achieved an AUC of 0.867 (95% CI 0.787-0.947) in the five-fold cross-validation, and this score was higher than that of the clinical model (AUC: 0.809; 95% CI: 0.715-0.902) and the radiomics model (AUC: 0.746; 95% CI: 0.681-0.811). The clinical-radiomics model showed an AUC of 0.822 in the test cohort, higher than that of the clinical model (AUC: 0.684, p = 0.007), radiomics model (AUC: 0.784, p = 0.415), MELD score (AUC: 0.529, p < 0.001), and ALBI score (AUC: 0.644, p = 0.016). The SHAP analysis showed that the first-order radiomics features, including first-order maximum 64 × 64, first-order 90th percentile 64 × 64, and first-order 10th percentile 32 × 32, were the most important features for PHLF prediction.An interpretable clinical-radiomics model based on 2D-SWE and clinical variables can help in predicting symptomatic PHLF in HCC.