Tong Xu, Li Jing
Department of Radiotherapy, the Third Affiliated Hospital of Qiqihar Medical University, Qiqihar City, Heilongjiang Province, China.
Eur J Radiol Open. 2022 May 16;9:100424. doi: 10.1016/j.ejro.2022.100424. eCollection 2022.
This research aims to predict the micro-vascular invasion and histopathologic grade of hepatocellular carcinoma with the CT-derived radiomics.
The clinical and image data of 82 patients were accessed from the TCGA-LIHC collection in The Cancer Imaging Archive. Then the radiomics features were extracted from the CT images. For obtaining the appropriate feature subset, the redundant features were removed by means of intra-class agreement analysis, the Student t test, LASSO-regression and support vector machine (SVM) Recursive feature elimination (SVM-RFE). Then several machine-learning-based classifiers including SVM and random forest (RF) were established. To accurately evaluate the tumor grade and MVI with the integration of the Radiomics and clinical insights, the nomogram-based clinical models were constructed. The diagnostic performance was evaluated with ROC analysis.
7 and 10 radiomics features were selected via LASSO regression and SVM-RFE for identifying the tumor grade with regard to 13 and 10 features selected via LASSO regression and SVM-RFE for evaluating the MVI. The combination of the classifier-RF and the selection strategy of SVM-RFE yielded the best performance for grading HCC (AUC: 0.898). Differently, the combination of the classifier-RF and the selection strategy of LASSO regression resulted in the best performance for identifying MVI (AUC: 0.876). Finally, two nomograms were constructed with radiomics score (Rscore) and clinical risk factors, which showed excellent predictive value for both tumor grade (AUC: 0.928) and MVI (AUC: 0.945).
CT-derived radiomics were valuable for noninvasively assessing the micro-vascular invasion and histopathologic grade of hepatocellular carcinoma.
本研究旨在利用CT衍生的放射组学预测肝细胞癌的微血管侵犯和组织病理学分级。
从癌症影像存档中的TCGA-LIHC数据集中获取82例患者的临床和影像数据。然后从CT图像中提取放射组学特征。为了获得合适的特征子集，通过类内一致性分析、学生t检验、LASSO回归和支持向量机（SVM）递归特征消除（SVM-RFE）去除冗余特征。然后建立了包括支持向量机和随机森林（RF）在内的几种基于机器学习的分类器。为了通过整合放射组学和临床见解准确评估肿瘤分级和微血管侵犯，构建了基于列线图的临床模型。通过ROC分析评估诊断性能。
通过LASSO回归和SVM-RFE分别选择了7个和10个放射组学特征用于识别肿瘤分级，通过LASSO回归和SVM-RFE分别选择了13个和10个特征用于评估微血管侵犯。分类器-RF与SVM-RFE选择策略的组合在肝癌分级方面表现最佳（AUC：0.898）。不同的是，分类器-RF与LASSO回归选择策略的组合在识别微血管侵犯方面表现最佳（AUC：0.876）。最后，利用放射组学评分（Rscore）和临床危险因素构建了两个列线图，它们对肿瘤分级（AUC：0.928）和微血管侵犯（AUC：0.945）均显示出优异的预测价值。
CT衍生的放射组学对于非侵入性评估肝细胞癌的微血管侵犯和组织病理学分级具有重要价值。
