Objective: 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). Methods: 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. Results: 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. Conclusion: An interpretable clinical–radiomics model based on 2D-SWE and clinical variables can help in predicting symptomatic PHLF in HCC.
目的：本研究旨在开发并验证一种基于二维剪切波弹性成像（2D-SWE）的可解释影像组学模型，用于预测肝细胞癌（HCC）患者肝切除术后症状性肝功能衰竭（PHLF）的发生风险。方法：连续纳入345例患者。在训练过程中采用五折交叉验证，并在独立测试队列中对模型进行评估。基于2D-SWE图像建立多区域影像组学模型以预测症状性PHLF。将临床特征整合至模型中，训练临床-影像组学联合模型。将影像组学模型和临床-影像组学模型与包含临床变量及其他临床预测指标（包括终末期肝病模型评分和清蛋白-胆红素评分）的临床模型进行比较。采用SHAP方法对影像组学模型进行事后可解释性分析。结果：临床-影像组学模型在五折交叉验证中获得的曲线下面积为0.867，优于临床模型的0.809和影像组学模型的0.746。在测试队列中，临床-影像组学模型的曲线下面积为0.822，显著高于临床模型、影像组学模型、终末期肝病模型评分和清蛋白-胆红素评分。SHAP分析显示，一阶影像组学特征对PHLF预测最为重要。结论：基于2D-SWE和临床变量的可解释临床-影像组学模型有助于预测HCC患者肝切除术后症状性PHLF的发生。
肿瘤(癌症)患者之家