Prognostic nomograms and risk classifications of outcomes in very early-stage hepatocellular carcinoma patients after hepatectomy

Abstract

Background: Numerous clinical models have been proposed to evaluate and predict recurrence and survival of hepatocellular carcinoma (HCC) patients in different stages after resection, but no model for very early-stage HCC.

Methods: The data of 661 very early-stage HCC patients after curative resection in our hospital were retrospectively reviewed. Kaplan-Meier curves and Cox proportional hazards regression models were used to analyze recurrence and survival. The risk classifications for recurrence and survival were established by using classification and regression tree analysis. The nomograms were constructed and validated using bootstrap resampling and an independent 186-patient validation cohort from the same institution.

Results: According to the results of multivariate analysis for prognosis after resection, decision trees and 3-stratification classifications that satisfactorily determined the risk of recurrence and survival were established. Based on these two risk classifications, a six-factor nomogram for predicting recurrence and a six-factor nomogram for predicting survival were created. The concordance indexes were 0.64 for recurrence nomogram, with a 95% confidence interval of 0.60-0.67, and 0.76 for survival nomogram, with a 95% confidence interval of 0.70-0.82. The calibration curves showed good agreement between the predictions made by the nomograms and the actual survival outcomes. These predicting results for recurrence and survival were better than three common classical HCC stages and were confirmed in the independent validation cohort.

Conclusions: The 3-stratification classifications enabled satisfactory risk evaluations of recurrence and survival, and the nomograms showed considerably accurate predictions of the risk of recurrence and survival in very early-stage HCC patients after curative resection.

Keywords: Hepatocellular carcinoma; Prognosis; Risk classification; Very early-stage.