Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model
Tian-Yi Xia1, Zheng-Hao Zhou1, Xiang-Pan Meng1
1From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, China 210009 (T.Y.X., X.P.M., J.H.Z., Q.Y., W.L.W., Y.C.W., T.Y.T., S.H.J.); Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China (Z.H.Z., J.X.); MR Scientific Marketing, Siemens Healthineers, Shanghai, China (Y.S.); Department of Radiology, The Third Affiliated Hospital of Nantong University, Nantong, China (T.Z.); Department of Radiology, The Xiangya Hospital of Central South University, Changsha, China (X.Y.L.); Department of Radiology, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, China (Y.L.); and Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China (W.B.X.).
Abstract:
Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.
