Raisi-Estabragh Zahra, Jaggi Akshay, Gkontra Polyxeni, McCracken Celeste, Aung Nay, Munroe Patricia B, Neubauer Stefan, Harvey Nicholas C, Lekadir Karim, Petersen Steffen E
National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.
Barts Health National Health Service (NHS) Trust, Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, United Kingdom.
Front Cardiovasc Med. 2021 Dec 22;8:763361. doi: 10.3389/fcvm.2021.763361. eCollection 2021.
Cardiovascular magnetic resonance (CMR) radiomics analysis provides multiple quantifiers of ventricular shape and myocardial texture, which may be used for detailed cardiovascular phenotyping. We studied variation in CMR radiomics phenotypes by age and sex in healthy UK Biobank participants. Then, we examined independent associations of classical vascular risk factors (VRFs: smoking, diabetes, hypertension, high cholesterol) with CMR radiomics features, considering potential sex and age differential relationships. Image acquisition was with 1.5 Tesla scanners (MAGNETOM Aera, Siemens). Three regions of interest were segmented from short axis stack images using an automated pipeline: right ventricle, left ventricle, myocardium. We extracted 237 radiomics features from each study using Pyradiomics. In a healthy subset of participants ( = 14,902) without cardiovascular disease or VRFs, we estimated independent associations of age and sex with each radiomics feature using linear regression models adjusted for body size. We then created a sample comprising individuals with at least one VRF matched to an equal number of healthy participants ( = 27,400). We linearly modelled each radiomics feature against age, sex, body size, and all the VRFs. Bonferroni adjustment for multiple testing was applied to all -values. To aid interpretation, we organised the results into six feature clusters. Amongst the healthy subset, men had larger ventricles with dimmer and less texturally complex myocardium than women. Increasing age was associated with smaller ventricles and greater variation in myocardial intensities. Broadly, all the VRFs were associated with dimmer, less varied signal intensities, greater uniformity of local intensity levels, and greater relative presence of low signal intensity areas within the myocardium. Diabetes and high cholesterol were also associated with smaller ventricular size, this association was of greater magnitude in men than women. The pattern of alteration of radiomics features with the VRFs was broadly consistent in men and women. However, the associations between intensity based radiomics features with both diabetes and hypertension were more prominent in women than men. We demonstrate novel independent associations of sex, age, and major VRFs with CMR radiomics phenotypes. Further studies into the nature and clinical significance of these phenotypes are needed.
心血管磁共振(CMR)影像组学分析可提供心室形状和心肌纹理的多个量化指标,可用于详细的心血管表型分析。我们在英国生物银行的健康参与者中研究了CMR影像组学表型随年龄和性别的变化。然后,我们研究了经典血管危险因素(VRF:吸烟、糖尿病、高血压、高胆固醇)与CMR影像组学特征的独立关联,并考虑了潜在的性别和年龄差异关系。图像采集使用1.5特斯拉扫描仪(MAGNETOM Aera,西门子)。使用自动化流程从短轴堆栈图像中分割出三个感兴趣区域:右心室、左心室、心肌。我们使用Pyradiomics从每项研究中提取了237个影像组学特征。在无心血管疾病或VRF的健康参与者子集(n = 14902)中,我们使用针对体型进行调整的线性回归模型估计年龄和性别与每个影像组学特征的独立关联。然后,我们创建了一个样本,其中包括至少有一个VRF的个体,并与相同数量的健康参与者(n = 27400)进行匹配。我们对每个影像组学特征与年龄、性别、体型和所有VRF进行线性建模。对所有p值应用Bonferroni多重检验校正。为了便于解释,我们将结果组织成六个特征簇。在健康子集中,男性的心室比女性更大,心肌更暗淡且纹理复杂性更低。年龄增长与心室变小以及心肌强度的更大变化有关。总体而言,所有VRF都与更暗淡、信号强度变化更少、局部强度水平更均匀以及心肌内低信号强度区域的相对占比更大有关。糖尿病和高胆固醇也与心室尺寸较小有关,这种关联在男性中比女性更为明显。VRF导致的影像组学特征改变模式在男性和女性中大致一致。然而,基于强度的影像组学特征与糖尿病和高血压之间的关联在女性中比男性更为突出。我们证明了性别、年龄和主要VRF与CMR影像组学表型之间新的独立关联。需要对这些表型的性质和临床意义进行进一步研究。