Raisi-Estabragh Zahra, Gkontra Polyxeni, Jaggi Akshay, Cooper Jackie, Augusto João, Bhuva Anish N, Davies Rhodri H, Manisty Charlotte H, Moon James C, Munroe Patricia B, Harvey Nicholas C, Lekadir Karim, Petersen Steffen E
NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.
Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom.
Front Cardiovasc Med. 2020 Dec 2;7:586236. doi: 10.3389/fcvm.2020.586236. eCollection 2020.
To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix. The sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50). CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.
使用包含多种病例组合的多中心多厂商数据集,评估心脏磁共振(CMR)影像组学特征在重测扫描中的可重复性。样本包括来自VOLUMES资源(thevolumesresource.com)的54项重测研究。根据预定义方案对图像进行分割,以在舒张末期和收缩末期选择三个感兴趣区域(ROI):右心室、左心室(LV)和LV心肌。我们从所有三个ROI中提取了影像组学形状特征,此外,还从LV心肌中提取了一阶和纹理特征。每项研究总共提取了280个特征。对于每个特征,我们计算了组内相关系数(ICC)、受试者内变异系数和平均相对差异。我们根据按特征类别、ROI和心脏相位分层的平均ICC对特征的稳健性进行排名,结果显示可重复性范围广泛。在所有特征类别和ROI中都存在具有良好和优异可重复性(ICC≥0.75)的特征。高比例的一阶和纹理特征具有优异的可重复性(ICC≥0.90),然而,这些类别中也包含可重复性最差的特征(ICC<0.50)。CMR影像组学特征具有广泛的可重复性。本文旨在为未来的研究人员提供参考,以指导临床CMR影像组学模型中最稳健特征的选择。需要在更大、更丰富的数据集中开展进一步工作,以进一步明确CMR影像组学的技术性能和临床效用。