Identification of predictive factors in hepatocellular carcinoma outcome: A longitudinal study

Oncol Lett. 2020 Jul;20(1):765-773. doi: 10.3892/ol.2020.11581. Epub 2020 Apr 28.

Abstract

Various surgical methods impact the prognosis of patients with hepatocellular carcinoma (HCC) differently. However, clinical guidelines remain inconsistent and the relative importance of predictors of survival outcomes requires further evaluation. The present study aimed to rank the importance of predictive factors that impact the survival outcomes of patients with HCC and to compare the prognosis associated with different surgical methods based on data obtained from the Surveillance, Epidemiology and End Results database. To achieve these aims, the present study used a random forest (RF) model to detect important predictive factors associated with survival outcomes in patients with HCC. Cox regression analysis was used to compare different surgery methods. The variables included in the Cox regression model were selected based on the Gini index calculated by the RF model. Using the RF model, the present study demonstrated that surgery method, tumor size and age were the first, second and third most important factors associated with HCC prognosis, respectively. Overall, patients who underwent local tumor destruction [(hazard ratio (HR)=0.48; 95% confidence interval (CI), 0.45-0.51; P<0.001)], wedge or segmental resection (HR, 0.31; 95% CI, 0.29-0.33; P<0.001), lobectomy (HR, 0.29, 95% CI, 0.27-0.31; P<0.001) or liver transplantation (HR, 0.16; 95% CI, 0.14-0.17; P<0.001) demonstrated improved overall survival time compared with those treated with surgery, with a gradual decreasing trend observed in HRs. The present study demonstrated that the surgical method used is the most important predictor of the survival outcomes of patients with HCC. Liver transplantation resulted in the best prognosis for patients with HCC, except for those with undifferentiated tumors or distant metastasis.

Keywords: carcinoma; data mining; hepatocellular; survival analysis.