正常的肺膜和小呼吸道疾病的局部异质性与COPD的严重程度和进展有关
Alexander J Bell1, Ravi Pal1, Wassim W Labaki2
1Department of Radiology, University of Michigan, 109 Zina Pitcher Place BSRB A506, Ann Arbor, MI, 48109-2200, USA.
通过参数响应映射 (PRM) 量化的小呼吸道疾病 (SAD) 和正常肺的拓特征可以预测COPD进展和肺. 这种PRM方法对早期检测肺气和COPD充满希望.
科学领域:
- 肺部医学 肺部医学
- 放射学 放射学是指放射学
- 计算生物学 计算生物学
背景情况:
- 小呼吸道疾病 (SAD) 是COPD中空气流阻塞的关键驱动因素,也是肺气的前体.
- 使用参数响应映射 (PRM) 建立了SAD的量化,但其在评估肺气和COPD进展方面的全部潜力需要进一步探索.
研究的目的:
- 为了评估PRM衍生的正常肺膜膜和SAD的拓特征.
- 评估这些特征作为肺气的替代品和COPD患者体力测量衰退的预测因素.
主要方法:
- 对正常肺部 (PRMNorm) 和功能性SAD (PRMfSAD) 的PRM指标来自COPDGene研究CT扫描 (n=8956).
- 计算了PRMNorm和PRMfSAD的体积密度 (V) 和欧勒-波恩卡雷特征 (χ) 地图,以评估拓.
- 用多变量回归和机器学习模型对FEV1下降预测分析了COPD严重程度,肺气和螺旋计的关联.
主要成果:
- PRMfSAD和PRMNorm的拓测量 (V和 χ) 独立地与肺气程度有关.
- χfSAD和VfSAD独立地与预测的FEV1%有关.
- 结合PRM拓的机器学习模型预测了5年的FEV1下降,AUC为0.69.
结论:
- 功能性SAD的拓特征和从PRM衍生出的正常肺围膜在肺功能和肺气方面具有独立的价值.
- 这些来自PRM的拓读数可以在机器学习模型中使用时预测未来的螺旋测量下降.
- 拓式PRM方法为早期识别肺气发作和COPD进展提供了一个有希望的方法.
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