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基于血液检测的肝癌发生天数预测模型。

Prediction Model with Reveals Number of Days to Develop Liver Cancer from Blood Test.

机构信息

Genome Medical Science Project, National Center for Global Health and Medicine, Ichikawa 272-8516, Japan.

Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan.

出版信息

Int J Mol Sci. 2023 Mar 1;24(5):4761. doi: 10.3390/ijms24054761.

Abstract

The development of liver cancer in patients with hepatitis B is a major problem, and several models have been reported to predict the development of liver cancer. However, no predictive model involving human genetic factors has been reported to date. For the items incorporated in the prediction model reported so far, we selected items that were significant in predicting liver carcinogenesis in Japanese patients with hepatitis B and constructed a prediction model of liver carcinogenesis by the Cox proportional hazard model with the addition of () genotypes. The model, which included four items-sex, age at the time of examination, alpha-fetoprotein level (logAFP) and presence or absence of -revealed an area under the receiver operating characteristic curve (AUROC) of 0.862 for HCC prediction within 1 year and an AUROC of 0.863 within 3 years. A 1000 repeated validation test resulted in a C-index of 0.75 or higher, or sensitivity of 0.70 or higher, indicating that this predictive model can distinguish those at high risk of developing liver cancer within a few years with high accuracy. The prediction model constructed in this study, which can distinguish between chronic hepatitis B patients who develop hepatocellular carcinoma (HCC) early and those who develop HCC late or not, is clinically meaningful.

摘要

乙型肝炎患者肝癌的发展是一个主要问题,已经有几种模型被报道可以预测肝癌的发生。然而,迄今为止,还没有涉及人类遗传因素的预测模型被报道。对于迄今为止报告的预测模型中包含的项目,我们选择了在预测日本乙型肝炎患者肝癌发生方面具有显著意义的项目,并通过 Cox 比例风险模型构建了一个肝癌发生预测模型,其中加入了()基因型。该模型包括性别、检查时的年龄、甲胎蛋白水平(logAFP)和是否存在-四个项目,对于 1 年内 HCC 的预测,其受试者工作特征曲线下面积(AUROC)为 0.862,对于 3 年内的 HCC 预测,AUROC 为 0.863。1000 次重复验证测试的 C 指数为 0.75 或更高,或灵敏度为 0.70 或更高,表明该预测模型可以以较高的准确性区分在几年内发生肝癌风险较高的患者。本研究构建的预测模型可以区分早期发生肝细胞癌(HCC)和晚期或不发生 HCC 的慢性乙型肝炎患者,具有重要的临床意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64c0/10003621/f99213e0d743/ijms-24-04761-g001.jpg

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