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Performance and effectiveness of hepatocellular carcinoma screening in individuals with HBsAg seropositivity in China: a multicenter prospective study

Nature Cancer volume 4, pages 1382–1394 (2023)Cite this article

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A Publisher Correction to this article was published on 11 September 2023

This article has been updated

Abstract

Current guidelines recommend hepatocellular carcinoma (HCC) surveillance for at-risk individuals, including individuals with hepatitis B virus infection. However, the performance and survival benefits of annual screening have not been evaluated through multicenter prospective studies in a Chinese population. Between 2017 and 2021, we included 14,426 participants with hepatitis B surface antigen seropositivity in an annual HCC screening study in China using a multicenter prospective design with ultrasonography and serum alpha-fetoprotein. After four rounds of screening and follow-up, the adjusted hazard ratios of death after correction for lead-time and length-time biases for screen-detected cancers at the prevalent and incident rounds were 0.74 (95% confidence interval = 0.60–0.91) and 0.52 (95% confidence interval = 0.40–0.68), respectively. A meta-analysis demonstrated that HCC screening was associated with improved survival after adjusting for lead-time bias. Our findings highlight the ‘real-world’ feasibility and effectiveness of annual HCC screening in community settings for the early detection of HCC and to improve survival.

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Fig. 1: Flowchart of the HCC screening cohort.
Fig. 2: Survival curves in patients with HCC.
Fig. 3: Forest plot showing the pooled estimates.

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Data availability

The datasets generated for the current study are not publicly available due to Chinese legal restrictions (Data Security Law of the People’s Republic of China), the willingness of other researchers and current ethical approval, but are available from the corresponding author (W.C.) upon reasonable request. All data from the systematic review and meta-analysis have been included in the main text and its source data files. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

All custom code used for these analyses is publicly available at https://github.com/ChangfaXia/HCCScreen/ without any restrictions. This includes the code for the meta-analysis, sojourn time estimation and bias correction, as appropriate. For the data analysis, R v.4.1.3 was used with base packages and the following additional packages: survival, ggplot2 and msm.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (81974492 and 82273721), the Innovation Fund for Medical Sciences of the Chinese Academy of Medical Sciences (2021-I2M-1-066) and the Sanming Project of Medicine in Shenzhen (SZSM201911015). The funders had no role in study design, data collection, data analysis, data interpretation, writing of the final manuscript or the decision to publish. We gratefully acknowledge the cooperation of all involved staff affiliated with cancer registries and the Centers for Disease Control and Prevention.

Author information

Author notes

  1. These authors contributed equally: Hongmei Zeng, Maomao Cao, Changfa Xia.

Authors and Affiliations

  1. National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

    Hongmei Zeng & Ruiying Fu

  2. Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

    Maomao Cao, Changfa Xia & Wanqing Chen

  3. State Key Laboratory of Molecular Oncology and Department of Immunology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

    Dongmei Wang, Kun Chen & Chunfeng Qu

  4. Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China

    Zheng Zhu

  5. Department of Cancer Epidemiology, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, People’s Republic of China

    Shaokai Zhang

  6. Department for Chronic Non-communicable Diseases Control, Jiangsu Provincial Center for Disease Control and Prevention (Public Health Research Institute of Jiangsu Province), Nanjing, People’s Republic of China

    Jinyi Zhou

  7. Department of Chronic Non-communicable Diseases Prevention and Treatment, Anhui Provincial Center for Disease Control and Prevention, Hefei, People’s Republic of China

    Huadong Wang

  8. Department of Chronic Non-communicable Diseases Prevention and Treatment, Yingdong Center for Disease Control and Prevention, Fuyang, People’s Republic of China

    Xianyun Qi

  9. Sheyang Center for Disease Control and Prevention, Yancheng, People’s Republic of China

    Shuguang Dai

  10. Binhai Center for Disease Control and Prevention, Yancheng, People’s Republic of China

    Yong Chen

  11. Dancheng Center for Disease Control and Prevention, Zhoukou, People’s Republic of China

    Zhong Sun

  12. Mengcheng Center for Disease Control and Prevention, Bozhou, People’s Republic of China

    Hao Ding

  13. Shenqiu Center for Disease Control and Prevention, Zhoukou, People’s Republic of China

    Qingwen Li

  14. Lingbi Center for Disease Control and Prevention, Suzhou, People’s Republic of China

    Hui Zhao

  15. Department of Nutrition, Harvard University, Cambridge, MA, USA

    Xuehong Zhang

  16. Department of Cardiology and Internal Medicine, College of Medical Sciences, SGMK University, Olsztyn, Poland

    Jakub Morze

  17. Vanke School of Public Health, Tsinghua University, Beijing, People’s Republic of China

    John S. Ji

  18. Department of Epidemiology and Biostatistics, Peking University, Beijing, People’s Republic of China

    Feng Sun

  19. The Daffodil Centre, The University of Sydney, Sydney, New South Wales, Australia

    Xueqin Yu

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  1. Hongmei Zeng
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Contributions

H. Zeng, C.Q. and W.C. designed the study and interpreted the data. H. Zeng wrote the first draft of the manuscript. C.X. and M.C. had primary responsibility for applying the analytical methods to produce estimates and create the figures and tables. H. Zeng and M.C. had primary responsibility for seeking, extracting and cleaning the data. C.Q., D.W., K.C., Z.Z., S.Z., J.Z., H.D., X.Q., S.D., Y.C., Z.S., H.D., Q.L. and H. Zhao conducted the technical implementation and monitored the clinical conduct of the study. M.C. and R.F. carried out the systematic review and meta-analysis. J.M., J.S.J., X.Z., X.Y. and F.S. provided critical feedback on the methods or results. W.C., H. Zeng and C.Q. had full access to all the data in the study. All authors reviewed the final manuscript draft and agreed with its content and conclusions.

Corresponding authors

Correspondence to Chunfeng Qu or Wanqing Chen.

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Extended data

Extended Data Fig. 1 The flowchart of the study.

a, The flowchart of the HCC screening cohort. b, The flowchart of the systematic review and meta-analysis.

Source data

Extended Data Fig. 2 Detection rates and early-detection rates of HCC.

a, Detection rate for all participants (n = 327 patients). b, Detection rate for men (n = 239 patients). c, Detection rate for women (n = 88 patients). d, Early-detection rate for all participants (n = 327 patients). e, Early-detection rate for men (n = 239 patients). f, Early-detection rate for women (n = 88 patients). g, Detection rates in cirrhotic and non-cirrhotic participants (n = 327 patients).

Source data

Extended Data Fig. 3 Subgroup analyses of screening effectiveness.

a, Stage-specific survival for screen-detected cancers with known stage at diagnosis (n = 323 patients). b, Association between screening and effectiveness of overall survival. Data are displayed as the hazard ratio ±95% CI (screening group n = 327 patients, control group n = 1,446 patients).

Source data

Extended Data Fig. 4 Sensitivity analyses of the meta-analysis.

a, Influence analysis by exclusion of one study at a time. Data are displayed as the hazard ratio ±95% CI (n = 27 studies). b, Influence analysis by exclusion of one study adjusted for lead-time bias at a time. Data are displayed as the hazard ratio ±95% CI (n = 13 studies). c, Funnel plot for assessment of publication bias (n = 28 studies). Data are displayed as the hazard ratio and standard error of log hazard ratio.

Source data

Extended Data Fig. 5 Subgroup analyses of the meta-analysis.

a, Subgroup analysis by screening interval (A). interval <12 months & ≥12 months. (B) interval <12 months alone. (C). interval ≥12 months alone. b, Subgroup analysis by etiology of HCC (A). HBV only. (B). HCV only. (C). HBV/HCV. Data are displayed as the hazard ratio ± 95% CI (n = 28 studies).

Source data

Extended Data Table 1 Characteristics of the studies included in the meta-analysis evaluating the association between screening and overall survival in patients with HCC
Full size table

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–9.

Source data

Source Data Fig. 1

Data for the screening performance of the HCC screening cohort.

Source Data Fig. 2

Survival data in patients with HCC with or without screening.

Source Data Fig. 3

Data for the forest plot showing the pooled estimates. a, Survival. b, Survival adjusted for lead-time bias.

Source Data Extended Data Fig.1

Data for the flowchart. a, Flowchart of the HCC screening cohort. b, Flowchart of the systematic review and meta-analysis.

Source Data Extended Data Fig. 2

Data for the detection and early detection rates of HCC. a, Detection rate for all participants. b, Detection rate for men. c, Detection rate for women. d, Early detection rate for all participants. e, Early detection rate for men. f, Early detection rate for women. g, Detection rates in participants with and without cirrhosis.

Source Data Extended Data Fig. 3

Data for the subgroup analyses of screening effectiveness. a, Stage-specific survival for patients with screen-detected cancers with known stage at diagnosis. b, Association between screening and effectiveness of overall survival.

Source Data Extended Data Fig. 4

Data for the sensitivity analyses of the meta-analysis. a, Influence analysis by exclusion of one study at a time. b, Influence analysis by exclusion of one study at a time, adjusted for lead-time bias. c, Funnel plot to assess publication bias.

Source Data Extended Data Fig. 5

Data for the subgroup analyses of the meta-analysis. a, Subgroup analysis according to screening interval. Interval <12 months and ≥12 months (A). Interval <12 months alone (B). Interval ≥12 months alone (C). b, Subgroup analysis according to the etiology of HCC. HBV only (A). HCV only (B). HBV/HCV (C).

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Zeng, H., Cao, M., Xia, C. et al. Performance and effectiveness of hepatocellular carcinoma screening in individuals with HBsAg seropositivity in China: a multicenter prospective study. Nat Cancer 4, 1382–1394 (2023). https://doi.org/10.1038/s43018-023-00618-8

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