Radiomics-based differentiation of hepatocellular carcinoma and dysplastic nodules using noncontrast abbreviated MRI

Background Noncontrast abbreviated magnetic resonance imaging (NC-AMRI) has emerged as a cost-effective alternative to ultrasound for hepatocellular carcinoma (HCC) surveillance; however, its diagnostic performance for differentiating dysplastic nodules (DNs) from HCC remains limited in the absence of contrast enhancement. This study investigated whether machine learning (ML)-based radiomics using noncontrast AMRI can improve discrimination between DNs and HCC. Methods This retrospective study included 189 patients with histopathologically confirmed hepatic nodules (41 DNs and 148 HCC). NC-AMRI, defined in this study as a contrast-free abbreviated protocol excluding dynamic contrast-enhanced phases, was performed using 3.0 T MRI systems and included a limited set of four noncontrast imaging sequences: in-phase, opposed-phase, T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Model performance in differentiating HCC and DNs was assessed using 5-fold stratified cross-validation on the full dataset. Radiomic features were extracted from manually segmented regions of interest using an Image Biomarker Standardization Initiative (ISBI) compliant PyRadiomics pipeline feature selection was performed using recursive feature elimination after multicollinearity filtering. Logistic regression, support vector machine, random forest, and extreme gradient boosting models were trained. Results In terms of diagnostic performance, the machine learning-based models for differentiating HCC from DNs showed AUC values of 0.798 for logistic regression, 0.790 for support vector machine, 0.781 for random forest, and 0.758 for extreme gradient boosting, with corresponding sensitivities of 0.712, 0.669, 0.698, and 0.741 and specificities of 0.756, 0.805, 0.756, and 0.610, respectively. Notably, the machine learning-based models also demonstrated good performance in distinguishing high-grade DNs from HCC, achieving AUC values of 0.783 for logistic regression, 0.751 for support vector machine, 0.800 for random forest, and 0.742 for extreme gradient boosting, with corresponding sensitivities of 0.799, 0.835, 0.727, and 0.799 and specificities of 0.700, 0.500, 0.700, and 0.600, respectively. Conclusions Radiomics-based machine learning enhances the diagnostic performance of NC-AMRI for differentiating HCC from DNs, complementing conventional imaging and addressing key limitations of AMRI surveillance.