Evaluation of the predictive accuracy of nine staging systems in patients with hepatocellular carcinoma: a multicenter, retrospective study in China

S Shibo Wang Y Yu Li S Shuang Leng C Chao Li H Hong Zhu (School of Life and Health Technology) J Jing Lv D Dayong Zheng H Haifeng Lin J Jianhong Zhong M Ming Zhao J Jun Xue P Peng Huang Z Zhiyu Chen (Shenzhen Key Laboratory of Solid State Batteries) Q Qian Zhu W Weijia Fang X Xiufeng Liu Y Yan Yang N Nanya Wang

Abstract

Abstract There are several staging systems for hepatocellular carcinoma (HCC); however, the best prognostic system for Chinese patients has not been established. The aim of this study was to evaluate nine staging systems and identify the best prognostic staging system for Chinese patients with HCC. This retrospective study included 1,495 patients with HCC from 2012 to 2022 in 17 hospitals in China. The predictive ability of nine HCC staging systems was evaluated based on homogeneity, monotonicity of gradients, Akaike’s information criterion, and area under the receiver operating characteristic curve. Overall survival at 1, 3 and 5 years was 59.90%, 40.34%, and 34.83%, respectively. Multivariable Cox analysis identified older age, multiple tumor nodules, maximum tumor size > 5 cm, macrovascular invasion, distant metastases, treatment modalities, worse performance status, low protein, high bilirubin, and high alpha-fetoprotein as independent prognostic factors. The Hong Kong Liver Cancer (HKLC) system showed the highest predictive accuracy for Chinese patients in hepatitis B virus-infected populations and in those undergoing non-curative treatment. The Cancer of the Liver Italian Program showed the best predictive ability in patients with hepatitis C virus infection or in the curative treatment subgroup. HKLC may be the most consistent and reliable prognostic model for patients with HCC in the Chinese population.

Article Details

Volume / Issue Vol. 1, Issue 1
Published June 04, 2026
ISSN 2045-2322
Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (18)

S

Shibo Wang

Y

Yu Li

S

Shuang Leng

C

Chao Li

H

Hong Zhu

School of Life and Health Technology

J

Jing Lv

D

Dayong Zheng

H

Haifeng Lin

J

Jianhong Zhong

M

Ming Zhao

J

Jun Xue

P

Peng Huang

Z

Zhiyu Chen

Shenzhen Key Laboratory of Solid State Batteries

Q

Qian Zhu

W

Weijia Fang

X

Xiufeng Liu

Y

Yan Yang

N

Nanya Wang