Objective:Hepatocellular carcinoma(HCC)prognosis involves multiple clinical factors.Although nomogram models targeting various clinical factors have been reported in early and locally advanced HCC,there are currently few studies on complete and effective prognostic nomogram models for stage Ⅳ HCC patients.This study aims to creat nomograms for cancer-specific survival(CSS)in patients at stage Ⅳ of HCC and developing a web predictive nomogram model to predict patient prognosis and guide individualized treatment. Methods:Clinicopathological information on stage Ⅳ of HCC between January,2010 and December,2015 was collected from the Surveillance,Epidemiology,and End Results(SEER)database.The patients at stage Ⅳ of HCC were categorized into ⅣA(without distant metastases)and ⅣB(with distant metastases)subgroups based on the presence of distant metastasis,and then the patients from both ⅣA and ⅣB subgroups were randomly divided into the training and validation cohorts in a 7:3 ratio.Univariate and multivariate Cox regression analyses were used to analyze the independent risk factors that significantly affected CSS in the training cohort,and constructed nomogram models separately for stage ⅣA and stage ⅣB patients based on relevant independent risk factors.Two nomogram's accuracy and discrimination were evaluated by receiver operator characteristic(ROC)curves and calibration curves.Furthermore,web-based nomogram models were developed specifically for stage ⅣA and stage ⅣB HCC patients by R software.A decision analysis curve(DCA)was used to evaluate the clinical utility of the web-based nomogram models. Results:A total of 3 060 patients were included in this study,of which 883 were in stage ⅣA,and 2 177 were in stage ⅣB.Based on multivariate analysis results,tumor size,alpha-fetoprotein(AFP),T stage,histological grade,surgery,radiotherapy,and chemotherapy were independent prognostic factors for patients with stage ⅣA of HCC;and tumor size,AFP,T stage,N stage,histological grade,lung metastasis,surgery,radiotherapy,and chemotherapy were independent prognostic factors for patients with stage ⅣB HCC.In stage ⅣA patients,the 3-,6-,9-,12-,15-,and 18-month areas under the ROC curves for the training cohort were 0.823,0.800,0.772,0.784,0.784,and 0.786,respectively;and the 3-,6-,9-,12-,15-,and 18-month areas under the ROC curves for the validation cohort were 0.793,0.764,0.739,0.773,0.798,and 0.799,respectively.In stage ⅣB patients,the 3-,6-,9-,and 12-month areas under the ROC curves for the training cohort were 0.756,0.750,0.755,and 0.743,respectively;and the 3-,6-,9-,and 12-month areas under the ROC curves for the validation cohort were 0.744,0.747,0.775,and 0.779,respectively;showing that the nomograms had an excellent predictive ability.The calibration curves showed a good consistency between the predictions and actual observations. Conclusion:Predictive nomogram models for CSS in stage ⅣA and ⅣB HCC patients are developed and validated based on the SEER database,which might be used for clinicians to predict the prognosis,implement individualized treatment,and follow up those patients.