目的：评估虚拟磁共振弹性成像（Virtual MR elastography, vMRE）术前预测巴塞罗那临床肝癌（Barcelona Clinic Liver Cancer , BCLC）A期（≤5.0 cm）肝细胞癌（Hepatocellular carcinoma, HCC）微血管侵犯（Microvascular invasion, MVI）的性能，并基于vMRE、多b值DWI模型和临床放射学（Clinical-radiological, CR）特征构建联合列线图。

方法： 前瞻性连续收集疑似HCC并于术前行多b值DWI检查的患者。获取vMRE、单指数模型、体素内不相干运动模型和扩散峰度成像模型的定量参数。采用多变量logistic回归分析确定MVI的独立预测因子并构建预测模型。利用独立的定量参数构建组合MRI评分模型（MRI_Score）。基于显著的CR特征和MRI_Score构建可视化列线图，并评估定量参数和模型的预测性能。

结果：本研究最终共纳入103例患者（男性87例，女性16例；范围：35–70岁）。基于扩散的剪切模量（μ_Diff）对MVI的受试者工作特征（receiver operating characteristic，ROC）曲线下面积（area under the curve，AUC）为0.735。利用真扩散系数（D）、平均峰度系数（MK）和μ_Diff构建了MRI_Score。CR模型和MRI_Score的AUC分别为0.787和0.840。基于甲胎蛋白（AFP）、晕环样强化、肿瘤包膜、肿瘤静脉侵犯双特征预测指标（Two-trait predictor of venous invasion, TTPVI）和MRI_Score的联合列线图显著提高了术前预测MVI的性能，AUC达到0.931(Delong test p < 0.05)。

结论： vMRE在预测BCLC A期HCC的MVI方面展现出巨大潜力。结合CR特征、vMRE和定量扩散参数的联合列线图显著提高了预测准确性，可为临床医生确定合适的治疗方案提供帮助。

Purpose: To evaluate the performance of virtual MR elastography (vMRE) for predicting microvascular invasion (MVI) in Barcelona Clinic Liver Cancer (BCLC) stage A (≤ 5.0 cm) hepatocellular carcinoma (HCC) and to construct a combined nomogram based on vMRE, multi-b-value DWI models, and clinical-radiological (CR) features.

Methods: Consecutive patients with suspected HCC who underwent multi-b-value DWI examinations were prospectively collected. Quantitative parameters from vMRE, mono-exponential, intravoxel incoherent motion, and diffusion kurtosis imaging models were obtained. Multivariate logistic regression was used to identify independent MVI predictors and build prediction models. A combined MRI_Score was constructed using independent quantitative parameters. A visualized nomogram was built based on significant CR features and MRI_Score. The predictive performance of quantitative parameters and models was evaluated.

Results: The study included 103 patients (87 males and 16 females; range: 35–70 years). Diffusion-based shear modulus (μ_Diff) exhibited a predictive performance for MVI with area under the curve (AUC) of 0.735. The MRI_Score was developed employing true diffusion coefficient (D), mean kurtosis (MK), and μ_Diff. CR model and MRI_Score achieved AUCs of 0.787 and 0.840, respectively. The combined nomogram based on AFP, corona enhancement, tumor capsule, TTPVI, and MRI_Score significantly improved the predictive performance to an AUC of 0.931 (Delong test p < 0.05).

Conclusions: vMRE exhibited great potential for predicting MVI in BCLC stage A HCC. The combined nomogram integrating CR features, vMRE, and quantitative diffusion parameters significantly improved the predictive accuracy and could potentially assist clinicians in identifying appropriate treatment options.