Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients.
Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients.
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DOI：
10.3389/fneur.2018.00687
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发表时间：
2018
影响因子：
3.4
通讯作者：
Lee JM
中科院分区：
文献类型：
--
作者：
Dhar R;Chen Y;An H;Lee JM
脑水肿有助于大脑半球卒中后神经功能的恶化和死亡，但仍然没有有效的方法来预防或准确预测其发生。大数据方法可以提供对脑水肿严重程度和时间进程的生物学变异性和遗传贡献的见解。这些方法需要对大量中风患者的水肿严重程度进行定量分析。我们提出，脑脊液（CSF）体积随时间的变化可能是水肿进展的敏感和动态标志物，可以通过常规CT扫描测量。为了促进和扩大这种方法，我们创建了一种机器学习算法，能够从中风患者的连续CT扫描中分割和测量CSF体积。我们现在展示了我们的初步处理管道的结果，该管道能够有效地从一项前瞻性纵向卒中研究中招募的155名受试者的初始队列中提取CSF体积。我们证明扫描之间的总颅骨体积配准具有高度的可重复性（R = 0.982），以及基线CSF体积与患者年龄的强相关性（作为脑萎缩的替代品，R = 0.725）。从基线到最终CT的CSF体积减少与梗死体积（R = 0.715）和中线移位程度相关（二次模型，p < 2.2 × 10−16）。我们利用广义估计方程（GEE）对CSF体积随时间的变化进行建模（使用线性和二次项），并根据年龄进行调整。该模型表明CSF体积随时间推移而减少（p < 2.2 × 10−13），并且在脑水肿患者中更低（p = 0.0004）。我们现在正在完全自动化这个管道，以便使用XNAT（可扩展神经影像存档工具包）平台快速分析来自多个站点的更大规模的中风患者队列。数千名患者的水肿动力学数据将有助于准确预测恶性水肿以及变异性建模和进一步了解影响水肿严重程度的遗传变异。
Cerebral edema contributes to neurological deterioration and death after hemispheric stroke but there remains no effective means of preventing or accurately predicting its occurrence. Big data approaches may provide insights into the biologic variability and genetic contributions to severity and time course of cerebral edema. These methods require quantitative analyses of edema severity across large cohorts of stroke patients. We have proposed that changes in cerebrospinal fluid (CSF) volume over time may represent a sensitive and dynamic marker of edema progression that can be measured from routinely available CT scans. To facilitate and scale up such approaches we have created a machine learning algorithm capable of segmenting and measuring CSF volume from serial CT scans of stroke patients. We now present results of our preliminary processing pipeline that was able to efficiently extract CSF volumetrics from an initial cohort of 155 subjects enrolled in a prospective longitudinal stroke study. We demonstrate a high degree of reproducibility in total cranial volume registration between scans (R = 0.982) as well as a strong correlation of baseline CSF volume and patient age (as a surrogate of brain atrophy, R = 0.725). Reduction in CSF volume from baseline to final CT was correlated with infarct volume (R = 0.715) and degree of midline shift (quadratic model, p < 2.2 × 10−16). We utilized generalized estimating equations (GEE) to model CSF volumes over time (using linear and quadratic terms), adjusting for age. This model demonstrated that CSF volume decreases over time (p < 2.2 × 10−13) and is lower in those with cerebral edema (p = 0.0004). We are now fully automating this pipeline to allow rapid analysis of even larger cohorts of stroke patients from multiple sites using an XNAT (eXtensible Neuroimaging Archive Toolkit) platform. Data on kinetics of edema across thousands of patients will facilitate precision approaches to prediction of malignant edema as well as modeling of variability and further understanding of genetic variants that influence edema severity.
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影响因子：
5.7
作者：
Gurney, Jenny;Olsen, Timothy;Marcus, Daniel S.
通讯作者：
Marcus, Daniel S.
影响因子：
5.7
作者：
Avants BB;Tustison NJ;Song G;Cook PA;Klein A;Gee JC
通讯作者：
Gee JC
影响因子：
5.7
作者：
Feigin VL;Krishnamurthi RV;Parmar P;Norrving B;Mensah GA;Bennett DA;Barker-Collo S;Moran AE;Sacco RL;Truelsen T;Davis S;Pandian JD;Naghavi M;Forouzanfar MH;Nguyen G;Johnson CO;Vos T;Meretoja A;Murray CJ;Roth GA;GBD 2013 Writing Group;GBD 2013 Stroke Panel Experts Group
通讯作者：
GBD 2013 Stroke Panel Experts Group
影响因子：
9.8
作者：
Gorgolewski KJ;Auer T;Calhoun VD;Craddock RC;Das S;Duff EP;Flandin G;Ghosh SS;Glatard T;Halchenko YO;Handwerker DA;Hanke M;Keator D;Li X;Michael Z;Maumet C;Nichols BN;Nichols TE;Pellman J;Poline JB;Rokem A;Schaefer G;Sochat V;Triplett W;Turner JA;Varoquaux G;Poldrack RA
通讯作者：
Poldrack RA
影响因子：
8.3
作者：
Larrue, V;vonKummer, R;Bluhmki, E
通讯作者：
Bluhmki, E