在肝硬化患者中构建基于贝叶斯网络的肝细胞癌风险预测模型
Ni Ma1, Jingwei Song1, Yuqing Yang2
1School of Public Health, Xinjiang Medical University, Urumqi, China.
Frontiers in oncology
|January 28, 2026
概括
这项研究确定了肝硬化患者肝癌进展的关键风险因素,包括乙型肝炎,总胆固醇升高和抗素III活性降低. 综合LASSO回归和贝叶斯网络模型准确地预测了早期干预的高风险个体.
科学领域:
- 肝病学和瘤学 肝病学和瘤学
- 临床数据分析 临床数据分析
- 生物标志物发现发现
背景情况:
- 肝硬化显著增加患肝细胞癌 (HCC) 的风险.
- 早期识别高风险患者对于及时干预和改善结果至关重要.
- 对于肝硬化患者中HCC进展的现有风险预测模型需要改进.
研究的目的:
- 为了调查住院肝硬化患者的临床数据.
- 为了确定肝硬化进展到HCC的风险因素.
- 建立一个强大的风险预测模型,用于早期识别高风险患者.
主要方法:
- 对1,128名住院肝硬化患者的临床数据进行了回顾性分析.
- 使用单变量和多变量逻辑回归识别风险因素.
- 构建一个结合LASSO回归和贝叶斯网络的预测模型.
主要成果:
- 女性性别是一个保护因素;乙型肝炎,总胆固醇 (TC) 升高和抗血素III (AT3) 活性降低被确定为HCC进展的危险因素.
- 与后勤回归 (AUC = 0.780) 相比,LASSO回归和贝叶斯网络模型的结合显示出更高的预测性能 (AUC = 0.857).
- 确定的主要预测因素包括PT,TT,MAO,ALP,PDW,CRP,CK-MB,Ca,P,TC,AFP,FT4,SCC和AT3,它们对恶性瘤的发展有直接和间接的影响.
结论:
- 性别,乙型肝炎,TC和AT3是肝炎患者中HCC的重要风险因素.
- 开发的风险预测模型整合了LASSO回归和贝叶斯网络,显示出强大的预测价值.
- 该模型为早期识别和分层高风险的肝硬化患者的HCC进展提供了科学基础.
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