The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research.
The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research.
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DOI：
10.3389/fninf.2021.597708
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发表时间：
2021
影响因子：
3.5
通讯作者：
Marcus DS
中科院分区：
文献类型：
--
作者：
Mohammadian Foroushani H;Dhar R;Chen Y;Gurney J;Hamzehloo A;Lee JM;Marcus DS
中风是全球死亡和残疾的主要原因之一。通过药物发现和评估卒中患者结局来减少这种疾病负担需要更广泛的卒中病理生理学特征，但导致结局的潜在生物学和遗传因素在很大程度上是未知的。弥补这一关键的知识差距需要更深入的表型分析，包括人口统计学、临床、基因组和成像特征的大规模整合。这种大数据方法将通过开发和运行处理管道来大规模提取中风相关表型来促进。每年有数百万中风患者接受常规脑成像，捕获关于中风相关损伤和结果的丰富数据集。卒中神经影像表型库（SNIPR）是基于开源XNAT成像信息学平台开发的一个多中心集中式成像库，用于存储全球卒中患者的临床计算机断层扫描（CT）和磁共振成像（MRI）扫描。该存储库的目的是：（i）存储、管理、处理和促进高价值卒中成像数据集的共享，（ii）实施容器化自动计算方法，以从贡献的图像中提取图像特征和疾病特异性特征，（iii）促进成像、基因组和临床数据的整合，以对卒中后并发症进行大规模分析;以及（iv）将SNIPR开发为面向数据科学家和临床研究者的协作平台。目前，SNIPR主持的研究项目包括缺血性和出血性卒中，数据来自2，246名受试者，来自华盛顿大学临床影像档案馆的6，149次成像会议，以及来自不同国家的合作者的贡献，包括芬兰，波兰和西班牙。此外，我们扩展了XNAT数据模型，以纳入相关临床特征，包括受试者人口统计学、卒中严重程度（NIH卒中量表）、卒中亚型（使用吐司分类）和结局[改良兰金量表（mRS）]。图像处理管道使用容器化模块部署在SNIPR上，这有助于大规模复制。第一个这样的流水线使用Meta深度学习扫描分类器从DICOM头部数据和图像数据中识别轴向脑CT扫描，将串行扫描配准到图谱，分割组织隔室，并计算CSF体积。由此产生的体积可用于量化缺血性卒中后脑水肿的进展。因此，SNIPR能够开发和验证管道，以自动提取成像表型并将其与临床数据相结合，其总体目标是广泛了解卒中进展和结局。
Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the underlying biologic and genetic factors contributing to outcomes are largely unknown. Remedying this critical knowledge gap requires deeper phenotyping, including large-scale integration of demographic, clinical, genomic, and imaging features. Such big data approaches will be facilitated by developing and running processing pipelines to extract stroke-related phenotypes at large scale. Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. The Stroke Neuroimaging Phenotype Repository (SNIPR) was developed as a multi-center centralized imaging repository of clinical computed tomography (CT) and magnetic resonance imaging (MRI) scans from stroke patients worldwide, based on the open source XNAT imaging informatics platform. The aims of this repository are to: (i) store, manage, process, and facilitate sharing of high-value stroke imaging data sets, (ii) implement containerized automated computational methods to extract image characteristics and disease-specific features from contributed images, (iii) facilitate integration of imaging, genomic, and clinical data to perform large-scale analysis of complications after stroke; and (iv) develop SNIPR as a collaborative platform aimed at both data scientists and clinical investigators. Currently, SNIPR hosts research projects encompassing ischemic and hemorrhagic stroke, with data from 2,246 subjects, and 6,149 imaging sessions from Washington University’s clinical image archive as well as contributions from collaborators in different countries, including Finland, Poland, and Spain. Moreover, we have extended the XNAT data model to include relevant clinical features, including subject demographics, stroke severity (NIH Stroke Scale), stroke subtype (using TOAST classification), and outcome [modified Rankin Scale (mRS)]. Image processing pipelines are deployed on SNIPR using containerized modules, which facilitate replicability at a large scale. The first such pipeline identifies axial brain CT scans from DICOM header data and image data using a meta deep learning scan classifier, registers serial scans to an atlas, segments tissue compartments, and calculates CSF volume. The resulting volume can be used to quantify the progression of cerebral edema after ischemic stroke. SNIPR thus enables the development and validation of pipelines to automatically extract imaging phenotypes and couple them with clinical data with the overarching aim of enabling a broad understanding of stroke progression and outcomes.
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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：
30.8
作者：
Malik R;Chauhan G;Traylor M;Sargurupremraj M;Okada Y;Mishra A;Rutten-Jacobs L;Giese AK;van der Laan SW;Gretarsdottir S;Anderson CD;Chong M;Adams HHH;Ago T;Almgren P;Amouyel P;Ay H;Bartz TM;Benavente OR;Bevan S;Boncoraglio GB;Brown RD Jr;Butterworth AS;Carrera C;Carty CL;Chasman DI;Chen WM;Cole JW;Correa A;Cotlarciuc I;Cruchaga C;Danesh J;de Bakker PIW;DeStefano AL;den Hoed M;Duan Q;Engelter ST;Falcone GJ;Gottesman RF;Grewal RP;Gudnason V;Gustafsson S;Haessler J;Harris TB;Hassan A;Havulinna AS;Heckbert SR;Holliday EG;Howard G;Hsu FC;Hyacinth HI;Ikram MA;Ingelsson E;Irvin MR;Jian X;Jiménez-Conde J;Johnson JA;Jukema JW;Kanai M;Keene KL;Kissela BM;Kleindorfer DO;Kooperberg C;Kubo M;Lange LA;Langefeld CD;Langenberg C;Launer LJ;Lee JM;Lemmens R;Leys D;Lewis CM;Lin WY;Lindgren AG;Lorentzen E;Magnusson PK;Maguire J;Manichaikul A;McArdle PF;Meschia JF;Mitchell BD;Mosley TH;Nalls MA;Ninomiya T;O'Donnell MJ;Psaty BM;Pulit SL;Rannikmäe K;Reiner AP;Rexrode KM;Rice K;Rich SS;Ridker PM;Rost NS;Rothwell PM;Rotter JI;Rundek T;Sacco RL;Sakaue S;Sale MM;Salomaa V;Sapkota BR;Schmidt R;Schmidt CO;Schminke U;Sharma P;Slowik A;Sudlow CLM;Tanislav C;Tatlisumak T;Taylor KD;Thijs VNS;Thorleifsson G;Thorsteinsdottir U;Tiedt S;Trompet S;Tzourio C;van Duijn CM;Walters M;Wareham NJ;Wassertheil-Smoller S;Wilson JG;Wiggins KL;Yang Q;Yusuf S;AFGen Consortium;Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium;International Genomics of Blood Pressure (iGEN-BP) Consortium;INVENT Consortium;STARNET;Bis JC;Pastinen T;Ruusalepp A;Schadt EE;Koplev S;Björkegren JLM;Codoni V;Civelek M;Smith NL;Trégouët DA;Christophersen IE;Roselli C;Lubitz SA;Ellinor PT;Tai ES;Kooner JS;Kato N;He J;van der Harst P;Elliott P;Chambers JC;Takeuchi F;Johnson AD;BioBank Japan Cooperative Hospital Group;COMPASS Consortium;EPIC-CVD Consortium;EPIC-InterAct Consortium;International Stroke Genetics Consortium (ISGC);METASTROKE Consortium;Neurology Working Group of the CHARGE Consortium;NINDS Stroke Genetics Network (SiGN);UK Young Lacunar DNA Study;MEGASTROKE Consortium;Sanghera DK;Melander O;Jern C;Strbian D;Fernandez-Cadenas I;Longstreth WT Jr;Rolfs A;Hata J;Woo D;Rosand J;Pare G;Hopewell JC;Saleheen D;Stefansson K;Worrall BB;Kittner SJ;Seshadri S;Fornage M;Markus HS;Howson JMM;Kamatani Y;Debette S;Dichgans M
通讯作者：
Dichgans M
影响因子：
3
作者：
Marcus, Daniel S.;Olsen, Timothy R.;Buckner, Randy L.
通讯作者：
Buckner, Randy L.
影响因子：
4.8
作者：
Liew SL;Zavaliangos-Petropulu A;Jahanshad N;Lang CE;Hayward KS;Lohse KR;Juliano JM;Assogna F;Baugh LA;Bhattacharya AK;Bigjahan B;Borich MR;Boyd LA;Brodtmann A;Buetefisch CM;Byblow WD;Cassidy JM;Conforto AB;Craddock RC;Dimyan MA;Dula AN;Ermer E;Etherton MR;Fercho KA;Gregory CM;Hadidchi S;Holguin JA;Hwang DH;Jung S;Kautz SA;Khlif MS;Khoshab N;Kim B;Kim H;Kuceyeski A;Lotze M;MacIntosh BJ;Margetis JL;Mohamed FB;Piras F;Ramos-Murguialday A;Richard G;Roberts P;Robertson AD;Rondina JM;Rost NS;Sanossian N;Schweighofer N;Seo NJ;Shiroishi MS;Soekadar SR;Spalletta G;Stinear CM;Suri A;Tang WKW;Thielman GT;Vecchio D;Villringer A;Ward NS;Werden E;Westlye LT;Winstein C;Wittenberg GF;Wong KA;Yu C;Cramer SC;Thompson PM
通讯作者：
Thompson PM
影响因子：
3.5
作者：
Foroushani HM;Hamzehloo A;Kumar A;Chen Y;Heitsch L;Slowik A;Strbian D;Lee JM;Marcus DS;Dhar R
通讯作者：
Dhar R