Shi Gaofeng, Han Xue, Wang Qi, Ding Yan, Liu Hui, Zhang Yunfei, Dai Yongming
Department of Radiology, Fourth Hospital of Hebei Medical University, Shijiazhuang 050000, People's Republic of China.
Department of Research Collaboration Hospital (MRI), Central Research Institute, United Imaging Healthcare, Shanghai 201800, People's Republic of China.
Cancer Manag Res. 2020 Jul 20;12:6019-6031. doi: 10.2147/CMAR.S262973. eCollection 2020.
To predict multiple prognostic factors of HCC including histopathologic grade, the expression of Ki67 as well as capsule formation with intravoxel incoherent motions imaging by extracting the histogram metrics.
A total of 52 patients with HCC were recruited with the MR examinations undertaken at a 3T scanner. Histogram metrics were extracted from IVIM-derived parametric maps. Independent student -test was performed to explore the differences in metrics across different subtypes of prognostic factors. Spearman correlation test was utilized to evaluate the correlations between the IVIM metrics and prognostic factors. ROC analysis was applied to evaluate the diagnostic performance.
According to the independent student -test, there were 18, 4, and 8 IVIM-derived histogram metrics showing the capability for differentiating the subtypes of histopathologic grade, Ki67, and capsule formation, respectively, with -values of less than 0.05. Besides, there existed a lot of significant correlations between IVIM metrics and prognostic factors. Finally, by integrating different histogram metrics showing significant differences between various subgroups together via establishing logistic regression based diagnostic models, greatest diagnostic power was obtained for grading HCC (AUC=0.917), diagnosing patients with highly expressed Ki67 (AUC=0.861) and diagnosing patients with capsule formation (AUC=0.839).
Multiple prognostic factors including histopathologic grade, Ki67 expression status, and capsule formation can be accurately predicted with assistance of histogram metrics sourced from a single IVIM scan.
通过提取直方图指标，预测肝细胞癌（HCC）的多个预后因素，包括组织病理学分级、Ki67表达以及包膜形成情况。
共招募了52例HCC患者，在3T扫描仪上进行了磁共振成像（MR）检查。从体素内不相干运动（IVIM）衍生的参数图中提取直方图指标。采用独立样本t检验来探讨不同预后因素亚型之间指标的差异。利用Spearman相关性检验评估IVIM指标与预后因素之间的相关性。应用ROC分析评估诊断性能。
根据独立样本t检验，分别有18个、4个和8个IVIM衍生的直方图指标能够区分组织病理学分级、Ki67和包膜形成的亚型，P值均小于0.05。此外，IVIM指标与预后因素之间存在许多显著相关性。最后，通过建立基于逻辑回归的诊断模型，将显示不同亚组之间存在显著差异的不同直方图指标整合在一起，获得了用于HCC分级（AUC = 0.917）、诊断Ki67高表达患者（AUC = 0.861）和诊断有包膜形成患者（AUC = 0.839）的最大诊断效能。
借助单次IVIM扫描获取的直方图指标，可以准确预测包括组织病理学分级、Ki67表达状态和包膜形成在内的多个预后因素。
