MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes.
MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes.
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DOI：
10.3389/fnins.2021.691244
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发表时间：
2021
影响因子：
4.3
通讯作者：
Rost NS
中科院分区：
文献类型：
--
作者：
Bretzner M;Bonkhoff AK;Schirmer MD;Hong S;Dalca AV;Donahue KL;Giese AK;Etherton MR;Rist PM;Nardin M;Marinescu R;Wang C;Regenhardt RW;Leclerc X;Lopes R;Benavente OR;Cole JW;Donatti A;Griessenauer CJ;Heitsch L;Holmegaard L;Jood K;Jimenez-Conde J;Kittner SJ;Lemmens R;Levi CR;McArdle PF;McDonough CW;Meschia JF;Phuah CL;Rolfs A;Ropele S;Rosand J;Roquer J;Rundek T;Sacco RL;Schmidt R;Sharma P;Slowik A;Sousa A;Stanne TM;Strbian D;Tatlisumak T;Thijs V;Vagal A;Wasselius J;Woo D;Wu O;Zand R;Worrall BB;Maguire JM;Lindgren A;Jern C;Golland P;Kuchcinski G;Rost NS
大脑结构完整性的神经影像测量被认为是大脑健康的替代品，但精确的评估需要专门的高级图像采集。通过定量描述常规图像，放射组学分析具有评估大脑健康的潜力。我们力求：（1）通过预测白色高信号负荷（WMH）来评价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。我们分析了一个多中心队列的4,163例急性缺血性卒中（AIS）患者，采用全脑和WMH分割的T2-FLAIR MR图像。放射组学特征提取自外观正常的脑组织（脑掩模-WMH掩模）。使用ElasticNet线性回归进行基于放射学的个性化WMH负荷预测。我们建立了WMH的放射组学特征，其具有预测WMH负荷的稳定选择特征，然后使用典型相关分析（CCA）将该特征与临床变量相关。放射组学特征可预测WMH负荷（R2 = 0.855 ± 0.011）。7对典型变量（CV）显著相关WMH的放射组学特征和临床性状，其各自的典型相关为0.81、0.65、0.42、0.24、0.20、0.15和0.15（FDR校正的p值CV 1 -6 < 0.001，p值CV 7 = 0.012）。临床CV 1主要受年龄的影响，CV 2受性别的影响，CV 3受吸烟史和糖尿病史的影响，CV 4受高血压的影响，CV 5受房颤和糖尿病的影响，CV 6受冠心病的影响，CV 7受冠心病和糖尿病的影响。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结构损伤，并与临床表型相关，表明每个心血管风险特征的不同放射摄影纹理异常。进一步的研究可以评估放射组学，以预测WMH的进展和随访中风患者的大脑健康。
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. 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). Radiomic features were predictive of WMH burden (R2 = 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 p-valuesCV1–6 < 0.001, p-valueCV7 = 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. 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.
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影响因子：
8.3
作者：
Etherton, Mark R.;Wu, Ona;Rost, Natalia S.
通讯作者：
Rost, Natalia S.
影响因子：
4.8
作者：
Shao, Yuan;Chen, Zhonghua;Gong, Xiangyang
通讯作者：
Gong, Xiangyang
影响因子：
3.4
作者：
Valdés Hernández MDC;González-Castro V;Chappell FM;Sakka E;Makin S;Armitage PA;Nailon WH;Wardlaw JM
通讯作者：
Wardlaw JM
影响因子：
10.9
作者：
Dubost, Florian;de Bruijne, Marleen;Schirmer, Markus D.
通讯作者：
Schirmer, Markus D.
影响因子：
5.7
作者：
Wang, Hao-Ting;Smallwood, Jonathan;Bzdok, Danilo
通讯作者：
Bzdok, Danilo