Li Xiaoming, Cheng Lin, Li Chuanming, Hu Xianling, Hu Xiaofei, Tan Liang, Li Qing, Liu Chen, Wang Jian
Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
Department of Neurosurgery, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
J Clin Transl Hepatol. 2022 Feb 28;10(1):63-71. doi: 10.14218/JCTH.2021.00023. Epub 2021 Jun 21.
The relationship between quantitative magnetic resonance imaging (MRI) imaging features and gene-expression signatures associated with the recurrence of hepatocellular carcinoma (HCC) is not well studied.
In this study, we generated multivariable regression models to explore the correlation between the preoperative MRI features and Golgi membrane protein 1 (GOLM1), SET domain containing 7 (SETD7), and Rho family GTPase 1 (RND1) gene expression levels in a cohort study including 92 early-stage HCC patients. A total of 307 imaging features of tumor texture and shape were computed from T2-weighted MRI. The key MRI features were identified by performing a multi-step feature selection procedure including the correlation analysis and the application of RELIEFF algorithm. Afterward, regression models were generated using kernel-based support vector machines with 5-fold cross-validation.
The features computed from higher specificity MRI better described GOLM1 and RND1 gene-expression levels, while imaging features computed from lower specificity MRI data were more descriptive for the SETD7 gene. The GOLM1 regression model generated with three features demonstrated a moderate positive correlation (<0.001), and the RND1 model developed with five variables was positively associated (<0.001) with gene expression levels. Moreover, RND1 regression model integrating four features was moderately correlated with expressed RND1 levels (<0.001).
The results demonstrated that MRI radiomics features could help quantify GOLM1, SETD7, and RND1 expression levels noninvasively and predict the recurrence risk for early-stage HCC patients.
定量磁共振成像（MRI）特征与肝细胞癌（HCC）复发相关的基因表达特征之间的关系尚未得到充分研究。
在本研究中，我们建立了多变量回归模型，以探讨术前MRI特征与92例早期HCC患者队列中高尔基体膜蛋白1（GOLM1）、含SET结构域7（SETD7）和Rho家族GTP酶1（RND1）基因表达水平之间的相关性。从T2加权MRI计算出总共307个肿瘤纹理和形状的成像特征。通过执行包括相关性分析和RELIEFF算法应用的多步骤特征选择程序来识别关键MRI特征。之后，使用基于核的支持向量机并进行5折交叉验证生成回归模型。
从更高特异性MRI计算出的特征能更好地描述GOLM1和RND1基因表达水平，而从较低特异性MRI数据计算出的成像特征对SETD7基因的描述性更强。由三个特征生成的GOLM1回归模型显示出中等程度的正相关（<0.001），由五个变量建立的RND1模型与基因表达水平呈正相关（<0.001）。此外，整合四个特征的RND1回归模型与RND1表达水平呈中等程度相关（<0.001）。
结果表明，MRI放射组学特征可帮助无创量化GOLM1、SETD7和RND1表达水平，并预测早期HCC患者的复发风险。
