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.
Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
Int J Cancer. 2021 Jun 15;148(12):2924-2934. doi: 10.1002/ijc.33487. Epub 2021 Feb 15.
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.
在过去的三十年中，中国在降低乙肝病毒感染率方面取得了快速进展，这与生活方式的快速变化和人口老龄化有关。我们旨在开发和验证一个最新的肝癌风险预测模型，该模型使用常规可获得的预测因素，并评估其在筛查指导中的适用性。我们使用来自中国慢性病前瞻性研究（CKB）的数据，共纳入了 486285 名参与者。该模型纳入了 15 个危险因素。使用灵活参数生存模型来估计肝癌的 10 年绝对风险。进行决策曲线分析以评估模型的净获益，以量化临床实用性。在 4814320 人年的随访期间，共有 2706 名参与者发生了肝癌。该模型在开发和验证数据集中均表现出了出色的区分度，其 C 统计量（95%CI）分别为 0.80（0.79-0.81）和 0.80（0.78-0.82），并且观察到的风险和预测风险之间的校准也很好。决策曲线分析表明，与目前筛查所有 HBsAg 携带者的指南相比，在 2%的 10 年风险阈值下，使用该模型选择进行筛查的参与者可以提高获益。与当前的癌症筛查指南相比，我们的模型对癌症筛查更敏感（28.17% vs 25.96%）。我们开发并验证了一个基于中国慢性病前瞻性研究的 CKB-PLR（基于中国慢性病前瞻性研究的肝癌风险预测）模型，以预测 HBsAg 阳性和阴性人群的肝癌绝对风险。该模型的应用有利于精确识别普通人群中的高危人群。
