Zhang Haiping, Guo Dajing, Liu Huan, He Xiaojing, Qiao Xiaofeng, Liu Xinjie, Liu Yangyang, Zhou Jun, Zhou Zhiming, Liu Xi, Fang Zheng
Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China.
GE Healthcare, Shanghai 201203, China.
Diagnostics (Basel). 2022 Apr 21;12(5):1043. doi: 10.3390/diagnostics12051043.
Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study aimed to evaluate whether magnetic resonance imaging (MRI) models based on radiomics features could further improve the ability to classify LR-M tumour subtypes. A total of 102 liver tumours were defined as LR-M by two radiologists based on LI-RADS and were confirmed to be HCC (n = 31) and non-HCC (n = 71) by surgery. A radiomics signature was constructed based on reproducible features using the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression algorithms with tenfold cross-validation. Logistic regression modelling was applied to establish different models based on T2-weighted imaging (T2WI), arterial phase (AP), portal vein phase (PVP), and combined models. These models were verified independently in the validation cohort. The area under the curve (AUC) of the models based on T2WI, AP, PVP, T2WI + AP, T2WI + PVP, AP + PVP, and T2WI + AP + PVP were 0.768, 0.838, 0.778, 0.880, 0.818, 0.832, and 0.884, respectively. The combined model based on T2WI + AP + PVP showed the best performance in the training cohort and validation cohort. The discrimination efficiency of each radiomics model was significantly better than that of junior radiologists’ visual assessment (p < 0.05; Delong). Therefore, the MRI-based radiomics models had a good ability to discriminate between HCC and non-HCC in LR-M tumours, providing more options to improve the accuracy of LI-RADS classification.
在肝脏影像报告和数据系统（LI-RADS）的M（LR-M）类肿瘤中，非侵入性地区分肝细胞癌（HCC）与其他原发性肝脏恶性肿瘤对于患者的治疗选择至关重要，但基于医学图像的视觉评估是一项极具挑战性的任务。本研究旨在评估基于影像组学特征的磁共振成像（MRI）模型是否能进一步提高对LR-M肿瘤亚型进行分类的能力。两名放射科医生根据LI-RADS将总共102例肝脏肿瘤定义为LR-M，经手术证实为HCC（n = 31）和非HCC（n = 71）。使用最大相关最小冗余（mRMR）和最小绝对收缩与选择算子（LASSO）逻辑回归算法以及十折交叉验证，基于可重复特征构建了影像组学特征。应用逻辑回归建模基于T2加权成像（T2WI）、动脉期（AP）、门静脉期（PVP）以及联合模型建立不同的模型。这些模型在验证队列中进行独立验证。基于T2WI、AP、PVP、T2WI + AP、T2WI + PVP、AP + PVP和T2WI + AP + PVP的模型的曲线下面积（AUC）分别为0.768、0.838、0.778、0.880、0.818、0.832和0.884。基于T2WI + AP + PVP的联合模型在训练队列和验证队列中表现最佳。每个影像组学模型的鉴别效率明显优于初级放射科医生的视觉评估（p < 0.05；德龙检验）。因此，基于MRI的影像组学模型在LR-M肿瘤中具有良好的区分HCC和非HCC的能力，为提高LI-RADS分类的准确性提供了更多选择。
