OBJECTIVES We aimed to evaluate the role of volumetric ADC (vADC) and volumetric venous enhancement (vVE) in predicting the grade of tumor differentiation in hepatocellular carcinoma (HCC). METHODS The study population included 136 HCC patients (188 lesions) who had baseline MR imaging and histopathological report. Measurements of vVE and vADC were performed on baseline MRI. Tumors were histologically classified into low-grade and high-grade groups. The parameters between the two groups were compared using Mann-Whitney U and chi-square tests for continuous and categorical parameters, respectively. Area under receiver operating characteristic (AUROC) was calculated to investigate the accuracy of vADC and vVE. Logistic regression and multivariable Cox regression were used to unveil the potential parameters associated with high-grade HCC and patient's survival, respectively. RESULTS Lesions with higher vADC values and a higher absolute vADC skewness were more likely to be high grade on histopathology assessment (p = 0.001 and p = 0.0291, respectively). Also, vVE showed a trend to be higher in low-grade lesions (p = 0.079). Adjusted multivariable model including vADC, vVE, and vADC skewness could strongly predict HCC degree of differentiation (AUROC = 83%). Additionally, a higher Child-Pugh score (HR = 2.39 [p = 0.02] for score 2 and HR = 3.47 [p = 0.001] for score 3), vADC skewness (HR = 1.52, p = 0.02; per increments in skewness), and tumor volume (HR = 1.1, p = 0.001; per 100 cm3 increments) showed the highest association with patients' survival. CONCLUSIONS vADC and vVE have the potential to accurately predict HCC differentiation. Additionally, some imaging features in combination with patients' clinical characteristics can predict patient survival. KEY POINTS • Volumetric functional MRI metrics can be considered as non-invasive measures for determining tumor histopathology in HCC. • Estimating patient survival based on clinical and imaging parameters can be used for modifying management approach and preventing unnecessary adverse events.

中文翻译：

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基线体积功能性MRI在预测肝细胞癌的组织病理学分级和患者生存中的作用。

目的我们旨在评估容积ADC（vADC）和容积静脉增强（vVE）在预测肝细胞癌（HCC）肿瘤分化程度中的作用。方法该研究人群包括136例HCC患者（188个病灶），他们均进行了基线MR成像和组织病理学报告。在基线MRI上进行vVE和vADC的测量。肿瘤在组织学上分为低等级和高等级组。使用Mann-Whitney U和卡方检验分别比较两组之间的参数的连续和分类参数。计算接收器工作特性（AUROC）下的面积以研究vADC和vVE的准确性。Logistic回归和多变量Cox回归分别用于揭示与高级别HCC和患者生存相关的潜在参数。结果在组织病理学评估中，具有更高vADC值和更高绝对vADC偏斜的病变更有可能是高等级的（分别为p = 0.001和p = 0.0291）。同样，vVE在低度病变中显示出更高的趋势（p = 0.079）。调整后的多变量模型（包括vADC，vVE和vADC偏斜度）可以强烈预测HCC的分化程度（AUROC = 83％）。此外，Child-Pugh得分更高（得分2的HR = 2.39 [p = 0.02]，得分3的HR = 3.47 [p = 0.001]），vADC偏斜度（HR = 1.52，p = 0.02；偏斜度的增加） ，肿瘤体积（HR = 1.1，p = 0.001；每100 cm3增量）显示与患者 生存。结论vADC和vVE具有准确预测HCC分化的潜力。另外，一些影像学特征结合患者的临床特征可以预测患者的生存。要点•容积功能性MRI指标可被视为确定HCC肿瘤组织病理学的非侵入性措施。•根据临床和影像学参数估算患者生存率可用于修改管理方法并防止不必要的不​​良事件。要点•容积功能性MRI指标可被视为确定HCC肿瘤组织病理学的非侵入性措施。•根据临床和影像学参数估算患者生存率可用于修改管理方法并防止不必要的不​​良事件。要点•容积功能性MRI指标可被视为确定HCC肿瘤组织病理学的非侵入性措施。•根据临床和影像学参数估算患者生存率可用于修改管理方法并防止不必要的不​​良事件。