In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: n = 125; validation dataset, n = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.
为了解决目前肝脏肿瘤病理分级主要依靠穿刺活检、手术病理取材等侵入式方法的问题，提出了一种在非增强核磁共振图像（MRI）上进行肝脏肿瘤病理分级的定量分析方法。首先对采集到的 MRI 图像，由医生在专业软件中人工分割出病灶部位，对这些病灶部位提取高通量的 328 维图像特征，包括灰度、形状、纹理、小波等特征，利用最小绝对收缩和选择运算符（LASSO）和交叉验证方法从中挑选出对病理分级最有价值的特征，组成影像组学模型并融合临床信息实现对肿瘤高、低分化分类的定量分析。在 170 位肝脏肿瘤患者的 MRI 图像（T1 加权图像和 T2 加权图像）上进行实验，通过计算接收者操作特征（ROC）曲线下面积（AUC）来衡量模型的预测性能。结果表明，基于高通量图像特征的 LASSO 回归定量分析方法，在训练集上获得 AUC 为 0.909，在测试集上 AUC 为 0.800。挑选出来的图像特征组成的影像学标签可以对高、低分化进行自动分类，从而为医生提供了一种非侵入的辅助诊断方法，有助于预后判断和治疗方案的制定。.
Keywords: LASSO regression; liver tumour; magnetic resonance image; pathological grading; radiomics.