Magnetic Resonance Imaging-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma

Abstract

Background: To investigate the performance of MRI-based radiomics signature in identifying glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC). Methods: An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts. A set of 853 radiomic features were extracted from delay-phase magnetic resonance (MR) images for each patient. Univariable analysis and fisher scoring were utilized for feature reduction. Subsequently, further forward stepwise feature selection and radiomics signature building were performed based on support vector machine (SVM). Incorporating with independent risk factors, a combined nomogram was developed by multivariable logistic regression modelling. The predictive performance of the nomogram was calculated using area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness. Findings: The radiomics signature consisting of ten selected features achieved satisfying prediction efficacy (training cohort: AUC=0·879, validation cohort: AUC=0·871). Additionally, the combined nomogram integrating independent clinical risk factor α-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0·926 and 0·914 in the training and validation cohorts respectively. The calibration curve and DCA confirmed the clinical usefulness of our nomogram. Interpretation: The proposed MR-based radiomics signature is strongly related to Glypican 3-positive (GPC3-positive). The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC. Funding: This research received financial support from the National Natural Science Foundation of China (No. 81227901, 81527805); Ministry of Science and Technology of China (2017YFC1308701, 2017YFC1309100, 2016YFC0102600, 2016YFA0100902, 2016YFC0103803, 2016YFA0201401, 2016YFC0103702, 2014CB748600 and 2016YFC0103001); Chinese Academy of Sciences (No. GJJSTD20170004 and QYZDJ-SSW-JSC005); Beijing Municipal Science & Technology Commission (No. Z161100002616022i¼ŒZ171100000117023); the Strategic Priority Research Program of Chinese Academy of Science (No. XDBS01000000). Declaration of Interest: The authors declare no conflicts of interest. Ethical Approval: This study was approved by our Institutional Review Board and written informed consent was waived for all participants.