Abstract：
This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.
摘要：
本研究旨在确定原发性肿瘤的影像学特征，并建立指示肝细胞癌（HCC）肝外转移的模型。177例HCC的对比增强计算机断层扫描（CT）图像，包括26个转移性（MET）和151个非转移性（非MET），进行回顾性收集和分析。对于每种情况，851个放射学特征，量化形状，强度,纹理,和动脉期最大肝癌肿瘤分割体积内的异质性，使用Pyradiacomics提取。数据集被随机分为训练集和测试集。进行了合成少数过采样技术（SMOTE）以将训练集扩展到145个MET和145个非MET病例。测试集由六个MET和六个非MET案例组成。外部验证集由从独立临床单位收集的20个MET和25个非MET病例组成。Logistic回归和支持向量机(SVM)模型的识别是基于使用逐步前向方法选择的特征,而深度卷积神经网络,视觉几何组16(VGG16)，直接使用CT图像进行训练。灰度大小区域矩阵（GLSZM）特征构成了八个选定的转移预测因子中的四个，这归因于它们对肿瘤异质性的感知。放射学逻辑回归模型在测试集上产生0.944的受试者工作特征曲线下面积（AUROC），在外部验证集上产生0.744的AUROC。Logistic回归显示与SVM在性能上没有显着差异，并且明显优于VGG16。作为肝外转移检查，如胸部CT和骨闪烁显像，是标准但详尽的，影像组学模型有助于一种经济有效的方法，将HCC患者分为这些检查的合格组。
