Development and Validation of a Diagnostic Model for AFP- Negative Hepatocellular Carcinoma

Abstract

Abstract Purpose：AFP appears to be negative about 30% of overall hepatocellular carcinoma (HCC). Our study aimed to develop a nomogram model to diagnose AFP negative HCC (AFPN-HCC). Patients and methods: The training set and the external validation set consisted of 516 and 456 objects. LASSO, univariate and multivariable logistic regression were performed to construct the model and then transformed into a visualized nomogram. We further used the receiver operating characteristic (ROC) curves, the calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) for validation. Results:Four variables included age, PIVKA-II, platelet (PLT) counts and prothrombin time(PT) were selected to establish the nomogram. The area under the curve (AUC) of the ROC to distinguish AFPN-HCC patients was 0.937(95%CI, 0.892-0.938) in training set and 0.942(95%CI, 0.921-0.963) using the validation set and indicated satisfactory discriminative ability of the model. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations. DCA and CIC showed that the nomogram was clinically useful. Conclusions:Our model was effective for discrimination of AFPN-HCC from control subsets, and might be helpful for the diagnosis for AFPN-HCC.