Most malignant hepatic nodules (MHNs) eventually progress to hepatocellular carcinoma (HCC). However, assessment of the risk of malignancy in high-risk groups of patients with hepatic nodules remains a challenge. This study aimed to develop and validate a simple scoring system to predict the risk of development of MHNs. 1144 patients with primary nodular lesions of hepatic were divided into training cohort and validation cohort. The nomogram model for predicting the risk of MHNs was established according to age, sex, nodule size, prothrombin time (PT), alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-II (PIVKA-II), γ-glutamine acyltransferase isoenzyme (γ-GT), alanine aminotransferase (ALT), total bile acid (TBA), and total bilirubin (TBIL) in training cohort by logistic regression and validated in validation cohort. The area under receiver operating characteristic curve (AUC) of the predictive model for diagnosing MHNs in training cohort was 0.969 (95% CI: 0.959-0.979), with sensitivity 93.38% and specificity 90.75%, and the AUC in the validation cohort was 0.986 (95% CI: 0.975-0.996), with sensitivity 90.81% and specificity 94.26%. The AUC, sensitivity, and specificity of this model for the diagnosis of early-stage HCC were 0.942, 88.64% and 87.35% in training cohort, and 0.956, 87.04% and 91.85% in validation cohort, respectively. We established a nomogram model that used intuitive data for reliably predicting the risk of MHNs, and this model also showed good diagnostic accuracy in predicting early-stage HCC.