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Poster display session

130P - Development of a liver cancer risk prediction model for the general population in china: A potential tool for screening


23 Nov 2019


Poster display session


Tumour Site

Hepatobiliary Cancers


Xiaoshuang Feng


Annals of Oncology (2019) 30 (suppl_9): ix42-ix67. 10.1093/annonc/mdz422


X. Feng1, N. Li1, G. Wang2, S. Chen3, Z. Lyu1, L. Wei1, X. Li1, Y. Wen1, E. Giovannucci4, S. Wu3, M. Dai1, J. He1

Author affiliations

  • 1 -, National Cancer Center / National Clinical Research Center for Cancer / Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021 - Beijing/CN
  • 2 Department Of Oncology, Kailuan General Hospital, 063000 - Tangshan/CN
  • 3 Health Department, Kailuan Group, 063000 - Tangshan/CN
  • 4 Department Of Epidemiology And Nutrition, Harvard T. H. Chan School of Public Health, Boston/US


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Abstract 130P


At the population level, it is useful to estimate the liver cancer risk based on lifestyle factors and regular biomarkers to encourage high-risk people to be screened. Therefore, we conducted the current study to develop a prediction model for the stratification of liver cancer risk among the general population in China.


112 440 subjects aged 20-80 years from Kailuan Cohort were included in the current study. A total of 326 incident primary liver cancer occurred during 913 078.51 person-years of follow-up. We used the Cox proportional hazards regression model to obtain coefficients for each predictor in the 8-year prediction models among a random two thirds of participants. The prediction models were validated in the remaining one third of participants. Hosmer-Lemeshow’s statistic and Harrell’s C-index were used to evaluate calibration and discrimination, respectively.


A full prediction model that comprised of nine predictors, including age, sex, smoking pack-years, alcohol drinking, tea consumption, diabetes and fasting blood glucose (FBG) level, total cholesterol (TC), alanine aminotransferase (ALT), and hepatitis B virus surface antigen (HBsAg), was derived. The model showed good calibration (χ2=3.57, P = 0.89) and discrimination (Harrell’s C-index=0.85; 95% confidence interval [CI]: 0.81, 0.88) in the validation data set.


A practical liver prediction model based on accessible indicators combined with lifestyle factors, regular blood biomarkers, and hepatitis virus status was developed, which allows to stratify the risk of liver cancer among the general population. Because the factors in this model are able to be acquired from questionnaire and blood detection, it has great potential to be translated into practical use for public health.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.


National Key R& D Program.


All authors have declared no conflicts of interest.

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