Evaluation of Preoperative Microvascular Invasion in Hepatocellular Carcinoma Through Multidimensional Parameter Combination Modeling Based on Gd-EOB-DTPA MRI
Han‐Dan Zhang, Xiaoming Li, Yuhan Zhang, Fang Hu, Liang Tan, Fang Wang, Jing Yang, Dajing Guo, Yang Xu, Xianling Hu, Chen Liu, Jian Wang
Abstract
Background and Aims: The study established and compared the efficacy of the clinicoradiological model, radiomics model and clinicoradiological-radiomics hybrid model in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using gadolinium ethoxybenzyl diethylene triaminepentaacetic acid (Gd-EOB-DTPA) enhanced MRI. Methods: This was a study that enrolled 602 HCC patients from two institutions. Least absolute shrinkage and selection operator (Lasso) method was used to screen for the most important clinicoradiological and radiomics features that predict MVI pre-operatively. Three machine learning algorithms were used to establish the clinicoradiological, radiomics, and clinicoradiological-radiomics hybrid models. Area under the curve (AUC) of receiver operating characteristic (ROC) curves and Delong's test were used to compare and quantify the predictive performance of the models. Results: <0.05). Conclusions: The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images based on Gd-EOB-DTPA-enhanced MRI can effectively predict MVI.