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. To evaluate the value of a radiomics nomogram for preoperative differentiating hepatocellular adenoma (HCA) from hepatocellular carcinoma (HCC) in the noncirrhotic liver. 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. 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. The CT-based radiomics nomogram has the potential to accurately differentiate HCA from HCC in the noncirrhotic liver.