Cardiology Department, James Cook University Hospital, Marton Road, Middlesbrough TS4 3BW, UK.
Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK.
Eur Heart J Cardiovasc Imaging. 2024 Mar 27;25(4):437-445. doi: 10.1093/ehjci/jead285.
Cardiac disease affects the heart non-uniformly. Examples include focal septal or apical hypertrophy with reduced strain in hypertrophic cardiomyopathy, replacement fibrosis with akinesia in an infarct-related coronary artery territory, and a pattern of scarring in dilated cardiomyopathy. The detail and versatility of cardiovascular magnetic resonance (CMR) imaging mean it contains a wealth of information imperceptible to the naked eye and not captured by standard global measures. CMR-derived heterogeneity biomarkers could facilitate early diagnosis, better risk stratification, and a more comprehensive prediction of treatment response. Small cohort and case-control studies demonstrate the feasibility of proof-of-concept structural and functional heterogeneity measures. Detailed radiomic analyses of different CMR sequences using open-source software delineate unique voxel patterns as hallmarks of histopathological changes. Meanwhile, measures of dispersion applied to emerging CMR strain sequences describe variable longitudinal, circumferential, and radial function across the myocardium. Two of the most promising heterogeneity measures are the mean absolute deviation of regional standard deviations on native T1 and T2 and the standard deviation of time to maximum regional radial wall motion, termed the tissue synchronization index in a 16-segment left ventricle model. Real-world limitations include the non-standardization of CMR imaging protocols across different centres and the testing of large numbers of radiomic features in small, inadequately powered patient samples. We, therefore, propose a three-step roadmap to benchmark novel heterogeneity biomarkers, including defining normal reference ranges, statistical modelling against diagnosis and outcomes in large epidemiological studies, and finally, comprehensive internal and external validations.
心脏疾病会导致心脏不均匀地受到影响。例如，在肥厚型心肌病中，会出现局灶性间隔或心尖部肥厚伴应变减少；在与梗死相关的冠状动脉区域中，会出现替代纤维化伴运动障碍；在扩张型心肌病中，则会出现瘢痕模式。心血管磁共振（CMR）成像的细节和多功能性意味着它包含了大量肉眼无法察觉的信息，这些信息无法通过标准的整体测量来捕捉。CMR 衍生的异质性生物标志物可以促进早期诊断、更好的风险分层以及更全面的治疗反应预测。小型队列和病例对照研究证明了结构和功能异质性测量的概念验证的可行性。使用开源软件对不同的 CMR 序列进行详细的放射组学分析，可以描绘出独特的体素模式，作为组织病理学变化的标志。同时，应用于新兴 CMR 应变序列的离散度测量方法描述了心肌不同部位的可变纵向、环向和径向功能。两种最有前途的异质性测量方法是原始 T1 和 T2 上区域标准差的平均绝对偏差和最大局部径向壁运动时间的标准差，在 16 节段左心室模型中称为组织同步指数。实际限制包括不同中心的 CMR 成像协议不标准化以及在小样本、功能不足的患者样本中测试大量放射组学特征。因此，我们提出了一个三步路线图来基准新的异质性生物标志物，包括定义正常参考范围、在大型流行病学研究中针对诊断和结果进行统计建模，以及最终进行全面的内部和外部验证。
