Fig. 4.

Nomogram to estimate the risk of HCC incidence in patients with HCV who achieved SVR. A: The nomogram-based prediction scoring model established using Logistic regression. To use the nomogram, find the position of each variable on the corresponding axis, draw a line to the points axis for the number of points, add the points from all of the variables, and draw a line from the total points axis to determine the HCC-free probabilities at the lower line of the nomogram. C-index (0.835) represents the prediction performance of our model is satisfied. Take a 55years man with diabetes, AFP>20 ng/dl, LSM < kPa, without NAFLD and achieved SVR with PR for example, his nomogram score will be 50 + 45+40 + 0+0, total 135 giving a 2-year HCC free probability of 98%. However, if he had NAFLD and DAAs treatment, his total score would be 50 + 45+30 + 15 = 180, giving a 2-year HCC free probability of 94%, an increase of cancer risk by 4%. B: Validity of the predictive performance of the nomogram. C: Two-year HCC incidence after eradication of HCV.

PLT: platelet; ALT: alanine aminotransferase; TBIL: total bilirubin; CRE: creatinine; AFP: Alpha-fetoprotein; BMI: body mass index; LSM: liver stiffness measurement; DM: diabetes mellitus.