Associating Preoperative MRI Features and Gene Expression Signatures of Early-stage Hepatocellular Carcinoma Patients using Machine Learning

Xiaoming Li; Lin Cheng; Chuanming Li; Xianling Hu; Xiaofei Hu; Liang Tan; Qing Li; Chen Liu; Jian Wang

doi:10.14218/JCTH.2021.00023

Associating Preoperative MRI Features and Gene Expression Signatures of Early-stage Hepatocellular Carcinoma Patients using Machine Learning

J Clin Transl Hepatol. 2022 Feb 28;10(1):63-71. doi: 10.14218/JCTH.2021.00023. Epub 2021 Jun 21.

Authors

Xiaoming Li¹, Lin Cheng¹, Chuanming Li¹, Xianling Hu¹, Xiaofei Hu¹, Liang Tan^{2

3}, Qing Li⁴, Chen Liu¹, Jian Wang¹

Affiliations

¹ Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
² Department of Neurosurgery, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
³ Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China.
⁴ MR Collaborations, Siemens Healthcare Ltd., Shanghai, China.

Abstract

Background and aims: 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.

Methods: 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.

Results: 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 (p<0.001), and the RND1 model developed with five variables was positively associated (p<0.001) with gene expression levels. Moreover, RND1 regression model integrating four features was moderately correlated with expressed RND1 levels (p<0.001).

Conclusions: 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.

Keywords: Gene-expression; Hepatocellular carcinoma; MRI; Radiomics feature.