白质高信号的MRI影像组学特征与临床表型相关。
MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes.
作者信息
Bretzner Martin, Bonkhoff Anna K, Schirmer Markus D, Hong Sungmin, Dalca Adrian V, Donahue Kathleen L, Giese Anne-Katrin, Etherton Mark R, Rist Pamela M, Nardin Marco, Marinescu Razvan, Wang Clinton, Regenhardt Robert W, Leclerc Xavier, Lopes Renaud, Benavente Oscar R, Cole John W, Donatti Amanda, Griessenauer Christoph J, Heitsch Laura, Holmegaard Lukas, Jood Katarina, Jimenez-Conde Jordi, Kittner Steven J, Lemmens Robin, Levi Christopher R, McArdle Patrick F, McDonough Caitrin W, Meschia James F, Phuah Chia-Ling, Rolfs Arndt, Ropele Stefan, Rosand Jonathan, Roquer Jaume, Rundek Tatjana, Sacco Ralph L, Schmidt Reinhold, Sharma Pankaj, Slowik Agnieszka, Sousa Alessandro, Stanne Tara M, Strbian Daniel, Tatlisumak Turgut, Thijs Vincent, Vagal Achala, Wasselius Johan, Woo Daniel, Wu Ona, Zand Ramin, Worrall Bradford B, Maguire Jane M, Lindgren Arne, Jern Christina, Golland Polina, Kuchcinski Grégory, Rost Natalia S
机构信息
J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States.
Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences and Cognition, University of Lille, Lille, France.
出版信息
Front Neurosci. 2021 Jul 12;15:691244. doi: 10.3389/fnins.2021.691244. eCollection 2021.
OBJECTIVE
Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes.
METHODS
We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA).
RESULTS
Radiomic features were predictive of WMH burden ( = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected -values < 0.001, -value = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes.
CONCLUSION
Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health.
目的
脑结构完整性的神经影像学测量被认为是脑健康的替代指标，但精确评估需要专门的先进图像采集。通过定量描述传统图像，放射组学分析在评估脑健康方面具有潜力。我们旨在：（1）通过预测白质高信号负荷（WMH）来评估放射组学以评估脑结构完整性，以及（2）揭示预测性放射组学特征与临床表型之间的关联。
方法
我们分析了一个多中心队列，其中包括4163例急性缺血性卒中（AIS）患者的T2-FLAIR MR图像，这些图像有全脑和WMH分割。从外观正常的脑组织（脑掩码-WMH掩码）中提取放射组学特征。使用弹性网络线性回归对个性化WMH负荷进行基于放射组学的预测。我们构建了一个具有稳定选择特征的WMH放射组学特征，该特征可预测WMH负荷，然后使用典型相关分析（CCA）将此特征与临床变量相关联。
结果
放射组学特征可预测WMH负荷（= 0.855±0.011）。七对典型变量（CV）使WMH的放射组学特征与临床特征显著相关，各自的典型相关系数分别为0.81、0.65、0.42、0.24、0.20、0.15和0.15（FDR校正后的p值<0.001，p值= 0.012）。临床CV1主要受年龄影响，CV2受性别影响，CV3受吸烟和糖尿病史影响，CV4受高血压影响，CV5受心房颤动（AF）和糖尿病影响，CV6受冠状动脉疾病（CAD）影响，CV7受CAD和糖尿病影响。
结论
从AIS患者的T2-FLAIR图像中提取的放射组学捕获了脑实质的微观结构损伤，并与临床表型相关，这表明每种心血管风险特征都有不同的放射学纹理异常。进一步的研究可以评估放射组学以预测WMH的进展以及用于卒中患者脑健康的随访。
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