结合放射学和基因组结核病门户网站数据,用于药物耐药性分析
Vy C B Bui1, Ziv Yaniv2, Michael Harris2
1Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
IEEE access : practical innovations, open solutions
|September 4, 2023
概括
结合胸部X射线和基因组数据,有助于结核病治疗预测. 整合宿主放射性和病原体基因组特征可以改善对耐药结核病 (DR-TB) 治疗时间的预测.
科学领域:
- 医疗信息学 医疗信息学
- 基因组学就是基因组学.
- 放射学 放射学是一门学科.
背景情况:
- 耐药结核病 (DR-TB) 构成了日益严重的全球健康威胁,治疗成功率约为60%.
- 药物耐药性的早期检测对于患者的治疗结果和控制传播至关重要.
- 结核病门户程序提供了DR-TB病例的有价值的,尽管异质的数据集,整合了临床,基因组和放射学信息.
研究的目的:
- 评估结合宿主放射性特征 (胸部X射线) 和病原体基因组特征的实用性,以确定药物易感性和预测结核病治疗持续时间.
- 为应对TB门户数据集中不平衡和高维数据所带来的挑战.
主要方法:
- 利用了来自结核病门户程序的多模式数据,包括胸部X射线衍生的放射性特征和病原体基因组数据.
- 开发和评估了分类模型,以区分敏感结核病 (DS-TB) 和耐药结核病 (DR-TB).
- 构建回归模型来预测第一个成功的药物治疗方案的长度,比较使用放射性,基因组或组合特征的性能.
主要成果:
- 通过单独使用基因组特征或与放射性特征相结合,实现了DR-TB/DS-TB识别的分类准确率为92.4%.
- 结合放射学和基因组特征,预测第一个成功治疗的持续时间的相对误差降低到22.0%,仅仅基因组特征的相对误差为25.6%.
- 仅仅放射性特征就为预测最常见药物组合的治疗时间提供了最佳的性能 (17.8%的相对误差).
结论:
- 虽然结合放射学和基因组数据并没有提高DR-TB/DS-TB分类的准确性,但它显著改善了治疗长度的预测.
- 仅仅放射性特征就在预测特定常见药物治疗方案的治疗持续时间方面表现出了卓越的表现.
- 该研究强调了整合各种数据模式的潜力,以优化结核病管理和患者护理.
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