Background <p>To develop and validate radiomics and deep learning models based on Gd-EOB-DTPA enhanced MRI for differentiation between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase (HBP).</p> Methods <p>112 patients from three hospitals were collected totally. 84 patients from hospital a and b with 54 HCCs and 30 FNHs randomly divided into a training cohort (<i>n</i> = 59: 38 HCC; 21 FNH) and an internal validation cohort (<i>n</i> = 25: 16 HCC; 9 FNH). A total of 28 patients from hospital c (<i>n</i> = 28: 20 HCC; 8 FNH) acted as an external test cohort. 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the pre-contrast phase (Pre), arterial phase (AP), portal venous phase (PP) and HBP images. 512 deep learning features were extracted from VOIs in the AP, PP and HBP images. Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) were used to select the useful features. Conventional, delta radiomics and deep learning models were established using machine learning algorithms (support vector machine [SVM] and logistic regression [LR]) and their discriminatory efficacy assessed and compared.</p> Results <p>The combined deep learning models demonstrated the highest diagnostic performance in both the internal validation and external test cohorts, with area under the curve (AUC) values of 0.965 (95% confidence interval [CI]: 0.906, 1.000) and 0.851 (95% CI: 0.620, 1.000) respectively. The conventional and delta radiomics models achieved AUCs of 0.944 (95% CI: 0.779–0.979) and 0.938 (95% CI: 0.836–1.000) respectively, which were not significantly different from the deep learning models or each other (<i>P</i> = 0.559, 0.256, and 0.137).</p> Conclusion <p>The combined deep learning models based on Gd-EOB-DTPA enhanced MRI may be useful for discriminating HCC from FNH showing iso-or hyperintensity in the HBP.</p>