Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster display session

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

Date

23 Nov 2019

Session

Poster display session

Topics

Tumour Site

Hepatobiliary Cancers

Presenters

Xiaoshuang Feng

Citation

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

Authors

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

Resources

Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 130P

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Funding

National Key R& D Program.

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.