Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.
School of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA.
Eur Radiol. 2020 Jul;30(7):3748-3758. doi: 10.1007/s00330-020-06742-8. Epub 2020 Mar 6.
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).
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.
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 cm increments) showed the highest association with patients' survival.
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.
• 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.
本研究旨在评估体素 ADC（vADC）和体素静脉增强（vVE）在预测肝细胞癌（HCC）分化程度中的作用。
本研究纳入了 136 例 HCC 患者（188 个病灶），这些患者均接受了基线 MRI 检查和组织病理学报告。在基线 MRI 上测量 vVE 和 vADC。肿瘤在组织学上分为低级别和高级别两组。使用 Mann-Whitney U 检验和卡方检验分别比较两组间的连续和分类参数。计算受试者工作特征曲线（AUROC）下面积以评估 vADC 和 vVE 的准确性。使用 logistic 回归和多变量 Cox 回归分别揭示与高级别 HCC 和患者生存相关的潜在参数。
在组织学评估中，vADC 值较高和 vADC 偏度绝对值较高的病灶更有可能为高级别（p=0.001 和 p=0.0291）。此外，vVE 在低级别病变中也有升高的趋势（p=0.079）。包括 vADC、vVE 和 vADC 偏度的调整后的多变量模型可以强烈预测 HCC 的分化程度（AUROC=83%）。此外，较高的 Child-Pugh 评分（评分 2 为 HR=2.39 [p=0.02]，评分 3 为 HR=3.47 [p=0.001]）、vADC 偏度（HR=1.52，p=0.02；偏度每增加 1 个单位）和肿瘤体积（HR=1.1，p=0.001；肿瘤体积每增加 100 cm 3）与患者的生存相关性最高。
vADC 和 vVE 具有准确预测 HCC 分化的潜力。此外，一些影像学特征与患者的临床特征相结合可以预测患者的生存。
