The purpose of this study was to investigate the efficacy of magnetic resonance imaging (MRI) radiomics in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). The clinical and MRI data of 129 pathologically confirmed HCC patients and 48 ICC patients treated at the Affiliated Hospital of North Sichuan Medical College between April 2016 and December 2021 were retrospectively analyzed. The patients were randomly divided at a ratio of 7:3 into a training group of 124 patients (90 with HCC and 34 with ICC) and a validation group of 53 patients (39 with HCC and 14 with ICC). Radiomic features were extracted from axial fat suppression T2-weighted imaging (FS-T2WI) and axial arterial-phase (AP) and portal-venous-phase (PVP) dynamic-contrast-enhanced MRI (DCE-MRI) sequences, and the corresponding datasets were generated. The least absolute shrinkage and selection operator (LASSO) method was used to select the best radiomic features. Logistic regression was used to establish radiomic models for each sequence (FS-T2WI, AP and PVP models), a clinical model for optimal clinical variables (C model) and a joint radiomics model (JR model) integrating the radiomics features of all the sequences as well as a radiomics–clinical model combining optimal radiomic features and clinical risk factors (RC model). The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). The AUCs of the FS-T2WI, AP, PVP, JR, C and RC models for distinguishing HCC from ICC were 0.693, 0.863, 0.818, 0.914, 0.936 and 0.977 in the training group and 0.690, 0.784, 0.727, 0.802, 0.860 and 0.877 in the validation group, respectively. The results of this study suggest that MRI-based radiomics may help noninvasively differentiate HCC from ICC. The model integrating the radiomics features and clinical risk factors showed a further improvement in performance.
本研究旨在探讨磁共振成像（MRI）影像组学在鉴别肝细胞癌（HCC）与肝内胆管癌（ICC）中的效能。回顾性分析了2016年4月至2021年12月期间在川北医学院附属医院接受治疗的129例经病理证实的HCC患者和48例ICC患者的临床及MRI资料。按7:3的比例将患者随机分为训练组（124例，含90例HCC和34例ICC）和验证组（53例，含39例HCC和14例ICC）。从轴位脂肪抑制T2加权成像（FS-T2WI）以及轴位动脉期（AP）和门静脉期（PVP）动态对比增强MRI（DCE-MRI）序列中提取影像组学特征，并生成相应数据集。采用最小绝对收缩与选择算子（LASSO）方法筛选最佳影像组学特征。通过逻辑回归分别建立各序列（FS-T2WI、AP和PVP模型）的影像组学模型、基于最佳临床变量的临床模型（C模型）、整合所有序列影像组学特征的联合影像组学模型（JR模型），以及融合最佳影像组学特征与临床危险因素的影像-临床联合模型（RC模型）。采用受试者工作特征曲线下面积（AUC）评估各模型的性能。在训练组中，FS-T2WI、AP、PVP、JR、C和RC模型区分HCC与ICC的AUC值分别为0.693、0.863、0.818、0.914、0.936和0.977；在验证组中，相应AUC值分别为0.690、0.784、0.727、0.802、0.860和0.877。本研究结果表明，基于MRI的影像组学可能有助于无创鉴别HCC与ICC，而融合影像组学特征与临床危险因素的模型显示出更优的鉴别性能。
肿瘤(癌症)患者之家