Pathological grading of Hepatocellular Carcinomas in MRI using a LASSO algorithm

Abstract

To investigate the predictive ability of Radiomics signature for preoperative pathological grading of Hepatocellular Carcinomas (HCC), the no contrast MRI images were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiomics features. Variable selection via LASSO and logistic regression were used to select the most-predictive Radiomics features for the pathological grading. Cross-Validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Overall, the prediction performances by Radiomics features showed statistically significant correlations with pathological grading, however, improvement on the prediction performance by combining T1WI and T2WI data, classification performance obtained the AUC 0.829 in training dataset and the AUC 0.742 in validation dataset. This study consisted of 170 consecutive patients (training dataset: n=125; validation dataset, n=45). The results showed Radiomics signature was developed and validated to be a significant predictor for discrimination HCC pathological grading, which may serve as a complementary tool for the preoperative tumour grading in HCC.