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Prediction and clinical utility of a liver cancer risk model in Chinese adults: A prospective cohort study of 0.5 million people.
Yu, Chengxiao; Song, Ci; Lv, Jun; Zhu, Meng; Yu, Canqing; Guo, Yu; Yang, Ling; Chen, Yiping; Chen, Zhengming; Jiang, Tao; Ma, Hongxia; Jin, Guangfu; Shen, Hongbing; Hu, Zhibin; Li, Liming.
Afiliação
  • Yu C; Department of Epidemiology, China International Cooperation Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Song C; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
  • Lv J; Department of Epidemiology, China International Cooperation Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Zhu M; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
  • Yu C; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
  • Guo Y; Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China.
  • Yang L; Department of Epidemiology, China International Cooperation Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Chen Y; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
  • Chen Z; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
  • Jiang T; Chinese Academy of Medical Sciences, Beijing, China.
  • Ma H; Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Jin G; Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Shen H; Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  • Hu Z; Department of Epidemiology, China International Cooperation Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Li L; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
Int J Cancer ; 148(12): 2924-2934, 2021 06 15.
Article em En | MEDLINE | ID: mdl-33521941
ABSTRACT
China has made rapid progress in reducing the incidence of HBV infection in the past three decades, along with a rapidly changing lifestyle and aging population. We aimed to develop and validate an up-to-date liver cancer risk prediction model with routinely available predictors and evaluate its applicability for screening guidance. Using data from the China Kadoorie Biobank, we included 486 285 participants in this analysis. Fifteen risk factors were included in the model. Flexible parametric survival models were used to estimate the 10-year absolute risk of liver cancer. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. A total of 2706 participants occurred liver cancer over the 4 814 320 person-years of follow-up. Excellent discrimination of the model was observed in both development and validation datasets, with c-statistics (95% CI) of 0.80 (0.79-0.81) and 0.80 (0.78-0.82) respectively, as well as excellent calibration of observed and predicted risks. Decision curve analysis revealed that use of the model in selecting participants for screening improved benefit at a threshold of 2% 10-year risk, compared to current guideline of screening all HBsAg carriers. Our model was more sensitive than current guideline for cancer screening (28.17% vs 25.96%). We developed and validated a CKB-PLR (Prediction for Liver cancer Risk Based on the China Kadoorie Biobank Study) model to predict the absolute risk of liver cancer for both HBsAg seropositive and seronegative populations. Application of the model is beneficial for precisely identifying the high-risk groups among the general population.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Hepatite B / Neoplasias Hepáticas Tipo de estudo: Etiology_studies / Guideline / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged País como assunto: Asia Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Hepatite B / Neoplasias Hepáticas Tipo de estudo: Etiology_studies / Guideline / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Male / Middle aged País como assunto: Asia Idioma: En Ano de publicação: 2021 Tipo de documento: Article