CT-Based Radiomics Nomogram: A Potential Tool for Differentiating Hepatocellular Adenoma From Hepatocellular Carcinoma in the Noncirrhotic Liver

Rationale and Objectives: To evaluate the value of a radiomics nomogram for preoperative differentiating hepatocellular adenoma (HCA) from hepatocellular carcinoma (HCC) in the noncirrhotic liver.Materials and Methods: One hundred and thirty-one patients with HCA (n = 46) and HCC (n = 85) were divided into a training set (n = 93) and a test set (n = 38). Clinical data and CT findings were analyzed. Radiomics features were extracted from the triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm and a radiomics score was calculated. Combined with the radiomics score and independent clinical factors, a radiomics nomogram was developed by multivariate logistic regression analysis. The performance of the radiomics nomogram was assessed by calibration, discrimination and clinical usefulness. Results: Gender, age, and enhancement pattern were the independent clinical factors. Three thousand seven hundred and sixty-eight features were extracted and reduced to 7 features as the optimal discriminators to build the radiomics signature. The radiomics nomogram (area under the curve [AUC], 0.96; 95% confidence interval [CI], 0.93-0.99) and the clinical factors model (AUC, 0.93; 95%CI, 0.88-0.99) showed better discrimination capability (p = 0.001 and 0.047) than the radiomics signature (AUC, 0.83; 95%CI, 0.74-0.92) in the training set. In the test set, the radiomics nomogram (AUC, 0.94; 95%CI, 0.87-1.00) performed better (p = 0.013) than the radiomics signature (AUC, 0.75; 95%CI, 0.59-0.91). Decision curve analysis showed the radiomics nomogram outperformed the clinical factors model and the radiomics signature in terms of clinical usefulness.Conclusion: The CT-based radiomics nomogram has the potential to accurately differentiate HCA from HCC in the noncirrhotic liver.Abbreviations: HCC hepatocellular carcinoma, HCA hepatocellular adenoma, FNH focal nodular hyperplasia, HH hepatic haemangioma, CC cholangiocarcinoma, HBV hepatitis B virus, HCV hepatitis C virus, AFP serum alpha-fetoprotein, AP arterial phase, PVP portal venous phase, DP delayed phase, OR odds ratio, CI confidence intervals, ROI region of interest, GLCM gray level co-occurrence matrix, GLRLM gray level run length matrix, GLDM gray level dependence matrix, GLSZM gray level size zone matrix, ICC inter- and intra- class correlation coefficient, mRMR maximum relevance minimum redundancy, LASSO least absolute shrinkage and selection operator, Rad-score radiomics score, ROC receiver operator characteristic, AUC area under the curve, DCA decision curve analysis