Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.
J Nucl Cardiol. 2023 Aug;30(4):1474-1483. doi: 10.1007/s12350-022-03179-y. Epub 2023 Jan 5.
The current proof-of-concept study investigates the value of radiomic features from normal 13N-ammonia positron emission tomography (PET) myocardial retention images to identify patients with reduced global myocardial flow reserve (MFR).
Data from 100 patients with normal retention 13N-ammonia PET scans were divided into two groups, according to global MFR (i.e., < 2 and ≥ 2), as derived from quantitative PET analysis. We extracted radiomic features from retention images at each of five different gray-level (GL) discretization (8, 16, 32, 64, and 128 bins). Outcome independent and dependent feature selection and subsequent univariate and multivariate analyses was performed to identify image features predicting reduced global MFR.
A total of 475 radiomic features were extracted per patient. Outcome independent and dependent feature selection resulted in a remainder of 35 features. Discretization at 16 bins (GL16) yielded the highest number of significant predictors of reduced MFR and was chosen for the final analysis. GLRLM_GLNU was the most robust parameter and at a cut-off of 948 yielded an accuracy, sensitivity, specificity, negative and positive predictive value of 67%, 74%, 58%, 64%, and 69%, respectively, to detect diffusely impaired myocardial perfusion.
A single radiomic feature (GLRLM_GLNU) extracted from visually normal 13N-ammonia PET retention images independently predicts reduced global MFR with moderate accuracy. This concept could potentially be applied to other myocardial perfusion imaging modalities based purely on relative distribution patterns to allow for better detection of diffuse disease.
本概念验证研究旨在探讨从正常 13N-氨正电子发射断层扫描（PET）心肌保留图像中提取的放射组学特征值在识别心肌整体血流储备（MFR）降低的患者中的价值。
根据定量 PET 分析得出的整体 MFR（即<2 和≥2），将 100 例正常保留 13N-氨 PET 扫描患者的数据分为两组。我们从保留图像中提取了五个不同灰度级（GL）离散化（8、16、32、64 和 128 个bins）的放射组学特征。进行了独立和依赖于结果的特征选择，以及随后的单变量和多变量分析，以识别预测整体 MFR 降低的图像特征。
每位患者共提取了 475 个放射组学特征。独立和依赖于结果的特征选择后，剩余了 35 个特征。16 个 bin 的离散化（GL16）产生了最多的 MFR 降低的显著预测因子，并被选为最终分析。GLRLM_GLNU 是最稳健的参数，在截断值为 948 时，其检测弥漫性受损心肌灌注的准确性、敏感性、特异性、阴性和阳性预测值分别为 67%、74%、58%、64%和 69%。
从视觉上正常的 13N-氨 PET 保留图像中提取的单个放射组学特征（GLRLM_GLNU）可以独立地以中等准确性预测整体 MFR 降低。该概念可以应用于其他基于相对分布模式的心肌灌注成像方式，以更好地检测弥漫性疾病。
