使用fMRI,尿液和血清生物标志物的多模式模型用于糖尿病病的分类和风险预后
Xian Shao1,2, Huanqing Xu3, Lianqin Chen1
1NHC Key Lab of Hormones and Development, Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
Diabetes, obesity & metabolism
|July 2, 2025
概括
这项研究开发了使用功能磁共振成像 (fMRI) 和生物标志物的先进多模式模型,以分类病症并预测患者的预后. 这些模型显示了改善病管理中的临床决策的巨大潜力.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 放射学 放射学是一门学科.
- 生物标志物发现发现
- 机器学习 机器学习
背景情况:
- 功能磁共振成像 (fMRI) 提供了对脏微变的非侵入性评估.
- 开发强大的分类和预后模型对于病管理至关重要.
研究的目的:
- 开发和验证病的多模式分类和预后模型.
- 整合fMRI,临床数据和多omics生物标志物,以提高预测性能.
主要方法:
- 在172名参与者身上使用高分辨率多参数fMRI (T2WI,BOLD,DTI).
- 应用机器学习算法来整合fMRI标记物,临床指标和血清/尿液生物标记物 (CD300LF,CST4,MMRN2,SERPINA1,l-glutamic acid dimethyl ester,类胆).
- 使用精度,灵敏度,特异性,精度,AUC和回忆来评估模型性能.
主要成果:
- 建立了六种分类模型;第6种模型 (多模式:fMRI + omics + 临床数据) 实现了最高的准确性 (0.923).
- 开发了五种预后模型;E模型表现出优异的性能,AUC为0.975 (2年) 和0.932 (3年).
- 模型E有效地识别了具有高风险预后的患者.
结论:
- 综合fMRI,omics和临床数据的多模式模型显示了病分类和预后的巨大潜力.
- 这些模型可以增强临床决策和患者管理策略.
- 这项研究强调了将先进的成像技术与精密脏病学多omics数据相结合的价值.
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