Anatomical sites (Takasaki’s segmentation) predicts the recurrence-free survival of hepatocellular carcinoma

Abstract

Background: Until now, several classification staging system and treatment algorithm for hepatocelluar carcinoma (HCC) has been presented. However, anatomical location is not taken into account in these staging systems. The aim of this study is to investigate whether anatomical sites could predict the postoperative recurrence of HCC patients. Methods: 294 HCC patients were enrolled in this retrospective study. A novel score classification based on anatomical sites was established by a Cox regression model and validated in the internal validation cohort. Results: HCC patients were stratified according to the novel score classification into three groups (score 0, score 1–3 and score 4–6). The predictive accuracy of the novel recurrence score for HCC patients as determined by the area under the receiver operating characteristic curves (AUCs) at 1, 3, and 5 years (AUCs 0.703, 0.706, and 0.605) was greater than that of the other representative classification systems. These findings were supported by the internal validation cohort. For patients with Barcelona Clinic Liver Cancer (BCLC) 0 and A stage, our data demonstrated that there was no significant difference in recurrence-free survival (RFS) between patients with score 0 and liver transplantation recipients. Additionally, we introduced this novel classification system to guide anatomical liver resection for centrally located liver tumors. Conclusion: The novel score classification may provide a reliable and objective model to predict the RFS of HCC after hepatic resection.