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Yedavalli V, Salim HA, Mei J, Lakhani DA, Balar A, Musmar B, Adeeb N, Hoseinyazdi M, Luna L, Deng F, Hyson NZ, Dmytriw AA, Guenego A, Faizy TD, Heit JJ, Albers GW, Lu H, Urrutia VC, Nael K, Marsh EB, Hillis AE, Llinas R. Decreased Quantitative Cerebral Blood Volume Is Associated With Poor Outcomes in Large Core Patients. Stroke 2024; 55:2409-2419. [PMID: 39185560 DOI: 10.1161/strokeaha.124.047483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/02/2024] [Accepted: 07/23/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND Recent large core trials have highlighted the effectiveness of mechanical thrombectomy (MT) in acute ischemic stroke with large vessel occlusion. Variable perfusion-imaging thresholds and poor Alberta Stroke Program Early Computed Tomography Score reliability underline the need for more standardized, quantitative ischemia measures for MT patient selection. We aimed to identify the computed tomography perfusion parameter most strongly associated with poor outcomes in patients with acute ischemic stroke-large vessel occlusion with significant ischemic cores. METHODS In this study from 2 comprehensive stroke centers from 2 comprehensive stroke centers within the Johns Hopkins Medical Enterprise (Johns Hopkins Hospita-East Baltimore and Bayview Medical Campus) from July 29, 2019 to January 29, 2023 in a continuously maintained database, we included patients with acute ischemic stroke-large vessel occlusion with ischemic core volumes defined as relative cerebral blood flow <30% and ≥50 mL on computed tomography perfusion or Alberta Stroke Program Early Computed Tomography Score <6. We used receiver operating characteristics to find the optimal cutoff for parameters like cerebral blood volume (CBV) <34%, 38%, 42%, and relative cerebral blood flow >20%, 30%, 34%, 38%, and time-to-maximum >4, 6, 8, and 10 seconds. The primary outcome was unfavorable outcomes (90-day modified Rankin Scale score 4-6). Multivariable models were adjusted for age, sex, diabetes, baseline National Institutes of Health Stroke Scale, intravenous thrombolysis, and MT. RESULTS We identified 59 patients with large ischemic cores. A receiver operating characteristic curve analysis showed that CBV<42% ≥68 mL is associated with unfavorable outcomes (90-day modified Rankin Scale score 4-6) with an area under the curve of 0.90 (95% CI, 0.82-0.99) in the total and MT-only cohorts. Dichotomizing at this CBV threshold, patients in the ≥68 mL group exhibited significantly higher relative cerebral blood flow, time-to-maximum >8 and 10 seconds volumes, higher CBV volumes, higher HIR, and lower CBV index. The multivariable model incorporating CBV<42% ≥68 mL predicted poor outcomes robustly in both cohorts (area under the curve for MT-only subgroup was 0.87 [95% CI, 0.75-1.00]). CONCLUSIONS CBV<42% ≥68 mL most effectively forecasts poor outcomes in patients with large-core stroke, confirming its value alongside other parameters like time-to-maximum in managing acute ischemic stroke-large vessel occlusion.
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Affiliation(s)
- Vivek Yedavalli
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Hamza Adel Salim
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Janet Mei
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Dhairya A Lakhani
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
- Neuroendovascular Program, Massachusetts General Hospital, Harvard University, Boston (D.A.L., A.A.D.)
| | - Aneri Balar
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Basel Musmar
- Department of Neurosurgery and Interventional Neuroradiology, Louisiana State University (B.M., N.A.)
| | - Nimer Adeeb
- Department of Neurosurgery and Interventional Neuroradiology, Louisiana State University (B.M., N.A.)
| | - Meisam Hoseinyazdi
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Licia Luna
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Francis Deng
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Nathan Z Hyson
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Adam A Dmytriw
- Neuroendovascular Program, Massachusetts General Hospital, Harvard University, Boston (D.A.L., A.A.D.)
- Neurovascular Centre, Departments of Medical Imaging and Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada (A.A.D.)
| | - Adrien Guenego
- Department of Diagnostic and Interventional Neuroradiology, Erasme University Hospital, Brussels, Belgium (A.G.)
| | - Tobias D Faizy
- Department of Radiology, Neuroendovascular Program, University Medical Center Münster, Germany (T.D.F.)
| | - Jeremy J Heit
- Department of Interventional Neuroradiology, Stanford Medical Center, Palo Alto, CA (J.J.H., G.W.A.)
| | - Gregory W Albers
- Department of Interventional Neuroradiology, Stanford Medical Center, Palo Alto, CA (J.J.H., G.W.A.)
| | - Hanzhang Lu
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Victor C Urrutia
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Kambiz Nael
- David Geffen School of Medicine at UCLA, Los Angeles, CA (K.N.)
| | - Elisabeth B Marsh
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Argye E Hillis
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
| | - Raf Llinas
- Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD (V.Y., H.A.S., J.M., D.A.L., A.B., M.H., L.L., F.D., N.Z.H., H.L., V.C.U., E.B.M., A.E.H., R.L.)
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Qu H, Tang H, Gao DY, Li YX, Zhao Y, Ban QQ, Chen YC, Lu L, Wang W. Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke. Front Neurol 2024; 15:1477811. [PMID: 39364421 PMCID: PMC11447964 DOI: 10.3389/fneur.2024.1477811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Accepted: 09/09/2024] [Indexed: 10/05/2024] Open
Abstract
Purpose Rapid diagnosis of acute ischemic stroke (AIS) is critical to achieve positive outcomes and prognosis. This study aimed to construct a model to automatically identify the infarct core based on non-contrast-enhanced CT images, especially for small infarcts. Methods The baseline CT scans of AIS patients, who had DWI scans obtained within less than 2 h apart, were included in this retrospective study. A modified Target-based deep learning model of YOLOv5 was developed to detect infarctions on CT. Randomly selected CT images were used for testing and evaluated by neuroradiologists and the model, using the DWI as a reference standard. Intraclass correlation coefficient (ICC) and weighted kappa were calculated to assess the agreement. The paired chi-square test was used to compare the diagnostic efficacy of physician groups and automated models in subregions. p < 0.05 was considered statistically significant. Results Five hundred and eighty four AIS patients were enrolled in total, finally 275 cases were eligible. Modified YOLOv5 perform better with increased precision (0.82), recall (0.81) and mean average precision (0.79) than original YOLOv5. Model showed higher consistency to the DWI-ASPECTS scores (ICC = 0.669, κ = 0.447) than neuroradiologists (ICC = 0.452, κ = 0.247). The sensitivity (75.86% vs. 63.79%), specificity (98.87% vs. 95.02%), and accuracy (96.20% vs. 91.40%) were better than neuroradiologists. Automatic model had better diagnostic efficacy than physician diagnosis in the M6 region (p = 0.039). Conclusion The deep learning model was able to detect small infarct core on CT images more accurately. It provided the infarct portion and extent, which is valuable in assessing the severity of disease and guiding treatment procedures.
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Affiliation(s)
- Hang Qu
- Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu Province, China
| | - Hui Tang
- Department of Health Science and Kinesiology, Georgia Southern University, Statesboro, GA, United States
| | - Dong-yang Gao
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, China
| | - Yong-xin Li
- Chinese Institute of Brain Research, Beijing, China
| | - Yi Zhao
- Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu Province, China
| | - Qi-qi Ban
- Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu Province, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing Medical University Affiliated First Hospital, Nanjing, China
| | - Lu Lu
- Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu Province, China
| | - Wei Wang
- Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu Province, China
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Wei J, Shang K, Wei X, Zhu Y, Yuan Y, Wang M, Ding C, Dai L, Sun Z, Mao X, Yu F, Hu C, Chen D, Lu J, Li Y. Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic stroke: a multicenter study. Eur Radiol 2024:10.1007/s00330-024-10960-9. [PMID: 39060495 DOI: 10.1007/s00330-024-10960-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 05/11/2024] [Accepted: 06/07/2024] [Indexed: 07/28/2024]
Abstract
OBJECTIVES The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL). METHODS This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians' readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts. RESULTS The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system's diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency. CONCLUSION The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention. CLINICAL RELEVANCE STATEMENT The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications. KEY POINTS The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS. This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan. 94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.
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Affiliation(s)
- Jianyong Wei
- School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, China
- Clinical Research Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200233, Shanghai, China
| | - Kai Shang
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200233, Shanghai, China
| | - Xiaoer Wei
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200233, Shanghai, China
| | - Yueqi Zhu
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200233, Shanghai, China
| | - Yang Yuan
- ShuKun (BeiJing) Technology Co., Ltd., Jinhui Bd, Qiyang Road, 100029, Beijing, China
| | - Mengfei Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, China
| | - Chengyu Ding
- ShuKun (BeiJing) Technology Co., Ltd., Jinhui Bd, Qiyang Road, 100029, Beijing, China
| | - Lisong Dai
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200233, Shanghai, China
| | - Zheng Sun
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200233, Shanghai, China
| | - Xinsheng Mao
- ShuKun (BeiJing) Technology Co., Ltd., Jinhui Bd, Qiyang Road, 100029, Beijing, China
| | - Fan Yu
- Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, Beijing, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, 100190, Beijing, China
| | - Jie Lu
- Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, Beijing, China
| | - Yuehua Li
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200233, Shanghai, China.
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Lambert J, Demeestere J, Dewachter B, Cockmartin L, Wouters A, Symons R, Boomgaert L, Vandewalle L, Scheldeman L, Demaerel P, Lemmens R. Performance of Automated ASPECTS Software and Value as a Computer-Aided Detection Tool. AJNR Am J Neuroradiol 2023; 44:894-900. [PMID: 37500286 PMCID: PMC10411841 DOI: 10.3174/ajnr.a7956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/14/2023] [Indexed: 07/29/2023]
Abstract
BACKGROUND AND PURPOSE ASPECTS quantifies early ischemic changes in anterior circulation stroke on NCCT but has interrater variability. We examined the agreement of conventional and automated ASPECTS and studied the value of computer-aided detection. MATERIALS AND METHODS We retrospectively collected imaging data from consecutive patients with acute ischemic stroke with large-vessel occlusion undergoing thrombectomy. Five raters scored conventional ASPECTS on baseline NCCTs, which were also processed by RAPID software. Conventional and automated ASPECTS were compared with a consensus criterion standard. We determined the agreement over the full ASPECTS range as well as dichotomized, reflecting thrombectomy eligibility according to the guidelines (ASPECTS 0-5 versus 6-10). Raters subsequently scored ASPECTS on the same NCCTs with assistance of the automated ASPECTS outputs, and agreement was obtained. RESULTS For the total of 175 cases, agreement among raters individually and the criterion standard varied from fair to good (weighted κ = between 0.38 and 0.76) and was moderate (weighted κ = 0.59) for the automated ASPECTS. The agreement of all raters individually versus the criterion standard improved with software assistance, as did the interrater agreement (overall Fleiss κ = 0.15-0.23; P < .001 and .39 to .55; P = .01 for the dichotomized ASPECTS). CONCLUSIONS Automated ASPECTS had agreement with the criterion standard similar to that of conventional ASPECTS. However, including automated ASPECTS during the evaluation of NCCT in acute stroke improved the agreement with the criterion standard and improved interrater agreement, which could, therefore, result in more uniform scoring in clinical practice.
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Affiliation(s)
- J Lambert
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Departments of Imaging and Pathology (J.L., B.D., P.D.)
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
| | - J Demeestere
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - B Dewachter
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Departments of Imaging and Pathology (J.L., B.D., P.D.)
| | - L Cockmartin
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
| | - A Wouters
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - R Symons
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Imelda Hospital (R.S.), Bonheiden, Belgium
| | - L Boomgaert
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
| | - L Vandewalle
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - L Scheldeman
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
| | - P Demaerel
- From the Departments of Radiology (J.L., B.D., L.C., R.S., L. B., P.D.)
- Departments of Imaging and Pathology (J.L., B.D., P.D.)
| | - R Lemmens
- Neurology (J.D., L.V., L.S., R.S.), University Hospitals Leuven, Leuven, Belgium
- Neuroscience (J.D., A.W., L.V., L.S., R.L.)
- Experimental Neurology (J.D., A.W., L.V., L.S., R.L.), Laboratory of Neurobiology, Katholieke Universiteit Leuven, University of Leuven, Leuven, Belgium
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Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, Kim JA, Sharma R, Schiff SJ, Rosen MS, Kalpathy-Cramer J, Iglesias Gonzalez JE, Kimberly WT, Sheth KN. Deep Learning Applications for Acute Stroke Management. Ann Neurol 2022; 92:574-587. [PMID: 35689531 DOI: 10.1002/ana.26435] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/04/2022] [Indexed: 11/08/2022]
Abstract
Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter-clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice-focused primer on DL. Next, we examine real-world examples of DL applications in pixel-wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter-rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022.
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Affiliation(s)
- Isha R Chavva
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Anna L Crawford
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Mercy H Mazurek
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Matthew M Yuen
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | | | - Sam Payabvash
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Gordon Sze
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Charles C Matouk
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Jennifer A Kim
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Steven J Schiff
- Departments of Neurosurgery, Engineering Science and Mechanics and Physics, Penn State University, University Park, PA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Juan E Iglesias Gonzalez
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - W Taylor Kimberly
- Department of Neurology, Division of Neurocritical Care, Massachusetts General Hospital, Boston, MA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT
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Yu Z, Chen Z, Yu Y, Zhu H, Tong D, Chen Y. An automated ASPECTS method with atlas-based segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106376. [PMID: 34500140 DOI: 10.1016/j.cmpb.2021.106376] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 08/21/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND PURPOSE As a simple and reliable systematic method to evaluate the early ischemic changes in the blood supply region of the middle cerebral artery of patients with ischemic stroke, the Alberta Stroke Program Early CT score (ASPECTS) can be used for rapid semi-quantitative evaluation of ischemic lesions, which is helpful to select potential candidates for intravenous and intra-arterial therapies, determine the thrombolytic effect and long-term prognosis. This method mainly relies on doctors' visual observation. However, due to different levels of doctor's experience, the poor inter-reader agreement may result in errors in the final ASPECTS. The purpose of this work was to propose an automated semi-quantitative method for the diagnosis of acute ischemic stroke based on non-contrast computed tomography (NCCT), to provide a reference for doctors in the diagnosis and evaluation. METHODS NCCT data from a total of 90 patients were included for auto-ASPECTS training and testing. After preprocessing CT images, the regions of interest (ROI) for ASPECTS were labeled using atlas-based segmentation. The mean difference, mean ratio and brain density shifts (BDS) of the corresponding regions of the contralateral brain were used as the standard for quantitative analysis. The auto-ASPECTS method was developed and validated to predict early ischemic changes whose performance was evaluated by the agreement (accuracy) of predictions and consensus scores of two observers. RESULTS A comparison was made among the results on mean difference, mean ratio, BDS and the combination of multiple parameters as the standard. The result of using BDS alone was relatively better than the result of using any other parameter alone or any combination of multiple parameters, and accuracy in the test set was 0.80. In the test set, accuracy with using different BDS thresholds increased by 6.67% compared with using the consistent BDS threshold. After dichotomy of auto-ASPECTS and consensus scores with the threshold of 7, the agreement of them was 83.3% and there was no significant difference between the two distributions (p = 0.344) in McNemar test. CONCLUSIONS The proposed auto-ASPECTS method for NCCT images can provide useful information for early diagnosis and evaluation of patients with acute ischemic stroke (AIS).
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Affiliation(s)
- Zechen Yu
- Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China
| | - Zhongping Chen
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Yang Yu
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Haichen Zhu
- Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China
| | - Dan Tong
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
| | - Yang Chen
- Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China.
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Study on Model Iterative Reconstruction Algorithm vs. Filter Back Projection Algorithm for Diagnosis of Acute Cerebral Infarction Using CT Images. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5000102. [PMID: 34394893 PMCID: PMC8360711 DOI: 10.1155/2021/5000102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/22/2021] [Indexed: 11/17/2022]
Abstract
The aim was to explore the application value of computed tomography (CT) perfusion (CTP) imaging based on the iterative model reconstruction (IMR) in the diagnosis of acute cerebral infarction (ACI). 80 patients with ACI, admitted to hospital, were selected as the research objects and divided randomly into a routine treatment group (group A) and a low-dose group (group B) (each group with 40 patients). Patients in group A were scanned at 80 kV–150 mAs, and the traditional filtered back projection (FBP) algorithm was employed to reconstruct the images; besides, 80 kV–30 mAs was adopted to scan the patients in group B, and the images were reconstructed by IMR1, IMR2, IMR3, iDose4 (a kind of hybrid iterative reconstruction technology), and FBP, respectively. The application values of different algorithms were evaluated by CTP based on the collected CTP images of patients and detecting indicators. The results showed that the gray and white matter CT value, SD value, SNR, CNR, and subjective image scores of patients in group B were basically consistent with those of group A (p > 0.05) after the IMR1 reconstruction, and the CT and SD of gray and white matter in patients from group B reduced steeply (p < 0.05), while SNR and CNR increased dramatically after IMR2 and IMR3 reconstruction in contrast to group A (p < 0.05). Furthermore, the cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) of contrast agent, and time to peak (TTP) of contrast agent in patients from group B after iDose4 and IMR reconstruction were basically the same as those of group A (p > 0.05). Therefore, IMR combined with low-dose CTP could obtain high-quality CTP images of the brain with stable perfusion indicators and low radiation dose, which could be clinically applied in the diagnosis of ACI.
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Löffler MT, Sollmann N, Mönch S, Friedrich B, Zimmer C, Baum T, Maegerlein C, Kirschke JS. Improved Reliability of Automated ASPECTS Evaluation Using Iterative Model Reconstruction from Head CT Scans. J Neuroimaging 2021; 31:341-347. [PMID: 33421036 DOI: 10.1111/jon.12810] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/30/2020] [Accepted: 11/02/2020] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND AND PURPOSE Iterative model reconstruction (IMR) has shown to improve computed tomography (CT) image quality compared to hybrid iterative reconstruction (HIR). Alberta Stroke Program Early CT Score (ASPECTS) assessment in early stroke is particularly dependent on high-image quality. Purpose of this study was to investigate the reliability of ASPECTS assessed by humans and software based on HIR and IMR, respectively. METHODS Forty-seven consecutive patients with acute anterior circulation large vessel occlusions (LVOs) and successful endovascular thrombectomy were included. ASPECTS was assessed by three neuroradiologists (one attending, two residents) and by automated software in noncontrast axial CT with HIR (iDose4; 5 mm) and IMR (5 and 0.9 mm). Two expert neuroradiologists determined consensus ASPECTS reading using all available image data including MRI. Agreement between four raters (three humans, one software) and consensus were compared using square-weighted kappa (κ). RESULTS Human raters achieved moderate to almost perfect agreement (κ = .557-.845) with consensus reading. The attending showed almost perfect agreement for 5 mm HIR (κHIR = .845), while residents had mostly substantial agreements without clear trends across reconstructions. Software had substantial to almost perfect agreement with consensus, increasing with IMR 5 and 0.9 mm slice thickness (κHIR = .751, κIMR = .777, and κIMR0.9 = .814). Agreements inversely declined for these reconstructions for the attending (κHIR = .845, κIMR = .763, and κIMR0.9 = .681). CONCLUSIONS Human and software rating showed good reliability of ASPECTS across different CT reconstructions. Human raters performed best with the reconstruction algorithms they had most experience with (HIR for the attending). Automated software benefits from higher resolution with better contrasts in IMR with 0.9 mm slice thickness.
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Affiliation(s)
- Maximilian T Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg im Breisgau, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Sebastian Mönch
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benjamin Friedrich
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christian Maegerlein
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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Abstract
PURPOSE OF REVIEW This article describes how imaging can be used by physicians in diagnosing, determining prognosis, and making appropriate treatment decisions in a timely manner in patients with acute stroke. RECENT FINDINGS Advances in acute stroke treatment, including the use of endovascular thrombectomy in patients with large vessel occlusion and, more recently, of IV thrombolysis in an extended time window, have resulted in a paradigm shift in how imaging is used in patients with acute stroke. This paradigm shift, combined with the understanding that "time is brain," means that imaging must be fast, reliable, and available around the clock for physicians to make appropriate clinical decisions. CT has therefore become the primary imaging modality of choice. Recognition of a large vessel occlusion using CT angiography has become essential in identifying patients for endovascular thrombectomy, and techniques such as imaging collaterals on CT angiography or measuring blood flow to predict tissue fate using CT perfusion have become useful tools in selecting patients for acute stroke therapy. Understanding the use of these imaging modalities and techniques in dealing with an emergency such as acute stroke has therefore become more important than ever for physicians treating patients with acute stroke. SUMMARY Imaging the brain and the blood vessels supplying it using modern tools and techniques is a key step in understanding the pathophysiology of acute stroke and making appropriate and timely clinical decisions.
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Cheng X, Su X, Shi J, Liu Q, Zhou C, Dong Z, Xing W, Lu H, Pan C, Li X, Yu Y, Zhang L, Lu G. Comparison of automated and manual DWI-ASPECTS in acute ischemic stroke: total and region-specific assessment. Eur Radiol 2020; 31:4130-4137. [PMID: 33247346 DOI: 10.1007/s00330-020-07493-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 09/14/2020] [Accepted: 11/10/2020] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To compare the DWI-Alberta Stroke Program Early Computed Tomography Score calculated by a deep learning-based automatic software tool (eDWI-ASPECTS) with the neuroradiologists' evaluation for the acute stroke, with emphasis on its performance on 10 individual ASPECTS regions, and to determine the reasons for inconsistencies between eDWI-ASPECTS and neuroradiologists' evaluation. METHODS This retrospective study included patients with middle cerebral artery stroke who underwent MRI from 2010 to 2019. All scans were evaluated by eDWI-ASPECTS and two independent neuroradiologists (with 15 and 5 years of experience in stroke study). Inter-rater agreement and agreement between manual vs. automated methods for total and each region were evaluated by calculating Kendall's tau-b, intraclass correlation coefficient (ICC), and kappa coefficient. RESULTS In total, 309 patients met our study criteria. For total ASPECTS, eDWI-ASPECTS and manual raters had a strong positive correlation (Kendall's tau-b = 0.827 for junior raters vs. eDWI-ASPECTS; Kendall's tau-b = 0.870 for inter-raters; Kendall's tau-b = 0.848 for senior raters vs. eDWI-ASPECTS) and excellent agreement (ICC = 0.923 for junior raters and automated scores; ICC = 0.954 for inter-raters; ICC = 0.939 for senior raters and automated scores). Agreement was different for individual ASPECTS regions. All regions except for M5 region (κ = 0.216 for junior raters and automated scores), internal capsule (κ = 0.525 for junior raters and automated scores), and caudate (κ = 0.586 for senior raters and automated scores) showed good to excellent concordance. CONCLUSION The eDWI-ASPECTS performed equally well as senior neuroradiologists' evaluation, although interference by uncertain scoring rules and midline shift resulted in poor to moderate consistency in the M5, internal capsule, and caudate nucleus regions. KEY POINTS • The eDWI-ASPECTS based on deep learning perform equally well as senior neuroradiologists' evaluations. • Among the individual ASPECTS regions, the M5, internal capsule, and caudate regions mainly affected the overall consistency. • Uncertain scoring rules and midline shift are the main reasons for regional inconsistency.
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Affiliation(s)
- XiaoQing Cheng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, 305 Zhongshan East Road, Nanjing, 210002, Jiangsu, China
| | - XiaoQin Su
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, 305 Zhongshan East Road, Nanjing, 210002, Jiangsu, China
| | - JiaQian Shi
- Department of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - QuanHui Liu
- Department of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - ChangSheng Zhou
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, 305 Zhongshan East Road, Nanjing, 210002, Jiangsu, China
| | - Zheng Dong
- Department of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Wei Xing
- Radiology Department, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - HaiTao Lu
- Radiology Department, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | | | | | | | - LongJiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, 305 Zhongshan East Road, Nanjing, 210002, Jiangsu, China.
| | - GuangMing Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, 305 Zhongshan East Road, Nanjing, 210002, Jiangsu, China. .,Department of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, China.
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11
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Qiu W, Kuang H, Teleg E, Ospel JM, Sohn SI, Almekhlafi M, Goyal M, Hill MD, Demchuk AM, Menon BK. Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT. Radiology 2020; 294:638-644. [PMID: 31990267 DOI: 10.1148/radiol.2020191193] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial role in the treatment of patients with acute ischemic stroke (AIS). Patients with extensive infarction are unlikely to benefit from thrombolysis or thrombectomy procedures. Purpose To develop an automated approach to detect and quantitate infarction by using non-contrast-enhanced CT scans in patients with AIS. Materials and Methods Non-contrast-enhanced CT images in patients with AIS (<6 hours from symptom onset to CT) who also underwent diffusion-weighted (DW) MRI within 1 hour after AIS were obtained from May 2004 to July 2009 and were included in this retrospective study. Ischemic lesions manually contoured on DW MRI scans were used as the reference standard. An automatic segmentation approach involving machine learning (ML) was developed to detect infarction. Randomly selected nonenhanced CT images from 157 patients with the lesion labels manually contoured on DW MRI scans were used to train and validate the ML model; the remaining 100 patients independent of the derivation cohort were used for testing. The ML algorithm was quantitatively compared with the reference standard (DW MRI) by using Bland-Altman plots and Pearson correlation. Results In 100 patients in the testing data set (median age, 69 years; interquartile range [IQR]: 59-76 years; 59 men), baseline non-contrast-enhanced CT was performed within a median time of 48 minutes from symptom onset (IQR, 27-93 minutes); baseline MRI was performed a median of 38 minutes (IQR, 24-48 minutes) later. The algorithm-detected lesion volume correlated with the reference standard of expert-contoured lesion volume in acute DW MRI scans (r = 0.76, P < .001). The mean difference between the algorithm-segmented volume (median, 15 mL; IQR, 9-38 mL) and the DW MRI volume (median, 19 mL; IQR, 5-43 mL) was 11 mL (P = .89). Conclusion A machine learning approach for segmentation of infarction on non-contrast-enhanced CT images in patients with acute ischemic stroke showed good agreement with stroke volume on diffusion-weighted MRI scans. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Nael in this issue.
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Affiliation(s)
- Wu Qiu
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Hulin Kuang
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Ericka Teleg
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Johanna M Ospel
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Sung Il Sohn
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Mohammed Almekhlafi
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Mayank Goyal
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Michael D Hill
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Andrew M Demchuk
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
| | - Bijoy K Menon
- From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.)
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Liebeskind DS, Derdeyn CP, Wechsler LR. STAIR X: Emerging Considerations in Developing and Evaluating New Stroke Therapies. Stroke 2019; 49:2241-2247. [PMID: 30355006 DOI: 10.1161/strokeaha.118.021424] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- David S Liebeskind
- From the Neurovascular Imaging Research Core and UCLA Stroke Center, Department of Neurology, University of California, Los Angeles (D.S.L.)
| | - Colin P Derdeyn
- Departments of Radiology and Neurology, University of Iowa Hospitals and Clinics (C.P.D.)
| | - Lawrence R Wechsler
- Department of Neurology, University of Pittsburgh Medical Center, PA (L.R.W.)
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Austein F, Wodarg F, Jürgensen N, Huhndorf M, Meyne J, Lindner T, Jansen O, Larsen N, Riedel C. Automated versus manual imaging assessment of early ischemic changes in acute stroke: comparison of two software packages and expert consensus. Eur Radiol 2019; 29:6285-6292. [DOI: 10.1007/s00330-019-06252-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 04/07/2019] [Accepted: 04/25/2019] [Indexed: 10/26/2022]
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