首页|Associating Preoperative MRI Features and Gene Expression Signatures of Early-stage Hepatocellular Carcinoma Patients using Machine Learning
Associating Preoperative MRI Features and Gene Expression Signatures of Early-stage Hepatocellular Carcinoma Patients using Machine Learning
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NETL
NSTL
万方数据
维普
Background and Aims: The relationship between quanti-tative magnetic resonance imaging (MRI) imaging features and gene-expression signatures associated with the recur-rence of hepatocellular carcinoma (HCC) is not well studied. Methods: In this study, we generated multivariable regres-sion models to explore the correlation between the preoper-ative 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 cor-relation 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 demon-strated a moderate positive correlation (p<0.001), and the RND1 model developed with five variables was positively as-sociated (p<0.001) with gene expression levels. Moreover, RND1 regression model integrating four features was mod-erately correlated with expressed RND1 levels (p<0.001). Conclusions: The results demonstrated that MRI radiomics features could help quantify GOLM1, SETD7, and RND1 ex-pression levels noninvasively and predict the recurrence risk for early-stage HCC patients.
NETL
NSTL
万方数据
维普
