Gopalan Deepa, Gibbs J Simon R
Imperial College Healthcare NHS Trust, London W12 0HS, UK.
Imperial College London, London SW7 2AZ, UK.
Diagnostics (Basel). 2020 Nov 25;10(12):1004. doi: 10.3390/diagnostics10121004.
Imaging plays a cardinal role in the diagnosis and management of diseases of the pulmonary circulation. Behind the picture itself, every digital image contains a wealth of quantitative data, which are hardly analysed in current routine clinical practice and this is now being transformed by radiomics. Mathematical analyses of these data using novel techniques, such as vascular morphometry (including vascular tortuosity and vascular volumes), blood flow imaging (including quantitative lung perfusion and computational flow dynamics), and artificial intelligence, are opening a window on the complex pathophysiology and structure-function relationships of pulmonary vascular diseases. They have the potential to make dramatic alterations to how clinicians investigate the pulmonary circulation, with the consequences of more rapid diagnosis and a reduction in the need for invasive procedures in the future. Applied to multimodality imaging, they can provide new information to improve disease characterization and increase diagnostic accuracy. These new technologies may be used as sophisticated biomarkers for risk prediction modelling of prognosis and for optimising the long-term management of pulmonary circulatory diseases. These innovative techniques will require evaluation in clinical trials and may in themselves serve as successful surrogate end points in trials in the years to come.
影像学在肺循环疾病的诊断和管理中起着至关重要的作用。在图像本身背后，每一幅数字图像都包含大量的定量数据，而这些数据在当前的常规临床实践中几乎未得到分析，而现在放射组学正在改变这一现状。使用诸如血管形态测定法（包括血管迂曲度和血管容积）、血流成像（包括定量肺灌注和计算流体动力学）以及人工智能等新技术对这些数据进行数学分析，正在为肺血管疾病复杂的病理生理学和结构 - 功能关系打开一扇窗口。它们有可能极大地改变临床医生研究肺循环的方式，其结果是未来诊断更快，并且减少侵入性检查的需求。应用于多模态成像时，它们可以提供新信息以改善疾病特征描述并提高诊断准确性。这些新技术可用作复杂的生物标志物，用于预后风险预测建模以及优化肺循环疾病的长期管理。这些创新技术需要在临床试验中进行评估，并且在未来几年它们自身可能成为试验中成功的替代终点。
