Nomogram predicting survival in patients with lymph node-negative hepatocellular carcinoma based on the SEER database and external validation.

Background: The relationship between lymph node (LN) status and survival outcome in hepatocellular carcinoma (HCC) is a highly controversial topic. The aim of this study was to investigate the prognostic factors in patients without LN metastasis (LNM) and to construct a nomogram to predict cancer-specific survival (CSS) in this group of patients.

Methods: We screened 6840 eligible HCC patients in the Surveillance, Epidemiology and End Results（SEER）database between 2010 and 2019 and randomized them into a training cohort and an internal validation cohort, and recruited 160 patients from Zhongnan Hospital of Wuhan University as an external validation cohort. Independent prognostic factors obtained from univariate and multivariate analysis were used to construct a nomogram prediction model. The concordance index (C-index), area under curve (AUC), calibration plots and decision curve analysis (DCA) were used to assess the predictive power and clinical application of the model.

Results: Univariate and multivariate analysis revealed age, gender, bone metastasis, lung metastasis, AFP, T stage, surgery and chemotherapy as independent prognostic factors. The C-index of the constructed nomogram for the training cohort, internal validation cohort and external validation cohort are 0.746, 0.740, and 0.777, respectively. In the training cohort, the AUC at 1-, 3-, and 5-year were 0.81, 0.800, and 0.800, respectively. Calibration curves showed great agreement between the actual observations and predictions for the three cohorts. The DCA results suggest that the nomogram model has more clinical application potential.

Conclusion: We constructed a nomogram to predict CSS in HCC patients without LNM. The model has been internally and externally validated to have excellent predictive performance and can help clinicians determine prognosis and make treatment decisions.