Noninvasively predict the micro-vascular invasion and histopathological grade of hepatocellular carcinoma with CT-derived radiomics.

Objectives: This research aims to predict the micro-vascular invasion and histopathologic grade of hepatocellular carcinoma with the CT-derived radiomics. Methods: 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. Results: 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). Conclusion: CT-derived radiomics were valuable for noninvasively assessing the micro-vascular invasion and histopathologic grade of hepatocellular carcinoma.