Abstract
PURPOSE: AFP appears to be negative in 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 included 294 AFPN-HCC patients, 159 healthy objects, 63 patients with chronic hepatitis B(CHB), and 64 patients with liver cirrhosis (LC). And the validation set enrolled 137 healthy controls objects, 47 CHB patients and 45 patients with LC. LASSO, univariate, and multivariable logistic regression analysis were performed to construct the model and then transformed into a visualized nomogram. The receiver operating characteristic (ROC) curves, the calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were further used for validation. RESULTS: Four variables including 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) in validation set. We also found that the model had high diagnostic value for small-size HCC (tumor size < 5 cm) (AUC = 0.886) and HBV surface antigen-positive AFPN-HCC (AUC = 0.883). CONCLUSIONS: Our model was effective for discrimination of AFPN-HCC from patients with benign liver diseases and healthy controls, and might be helpful for the diagnosis for AFPN-HCC.