Yannan Yu

All Publications

• Absent filling of the superficial middle cerebral vein is associated with reperfusion but not parenchymal hematoma in stroke patients undergoing thrombectomy: an observational study ANNALS OF TRANSLATIONAL MEDICINE Zhang, S., Zhang, R., Jin, B., Shi, Z., Li, C., Yu, Y., Wang, Z. 2020; 8 (21): 1410

  Abstract

  Parenchymal hematoma (PH) is the most feared complication of reperfusion therapy after stroke. The opacification of the superficial middle cerebral vein (SMCV) on computed tomography perfusion (CTP) has been associated with poor functional outcomes after stroke, while its association with PH has not been verified for acute stroke patients undergoing thrombectomy.Consecutive patients with acute anterior large artery occlusion (LAO) who received thrombectomy were retrospectively enrolled between May 2018 and May 2019. Absent filing of the SMCV (SMCV-) on CTP-derived CT angiography was defined as no contrast filling of the SMCV across the whole venous phase in the ischemic hemisphere, while SMCV+ was defined as the presence of contrast filling of the SMCV at any time point of the venous phase.A total of 52 patients were enrolled in the study, and 15 patients (28.8%) developed a PH within 48 hours after thrombectomy. SMCV- was not associated with PH in both the univariate and multivariate logistic regression analyses (all P>0.05), but was an independent risk factor for reperfusion [modified thrombolysis in cerebral infarction score of 2b-3; odds ratio (OR) =0.172, 95% confidence interval (CI): 0.031-0.960, P=0.045]. Reperfusion was associated with a reduced risk of PH (OR =0.110, 95% CI: 0.013-0.913, P=0.041). However, in a subgroup analysis of patients who had reperfusion, the SMCV- group had a higher rate of PH than the SMCV+ group (40.0% vs. 13.8%, P=0.049).In patients who received thrombectomy, SMCV- did not predict PH, but was a risk factor for reperfusion. Although reperfusion was a protective factor for PH, the SMCV- group was still at a higher risk of PH compared with the SMCV+ group when reperfusion was successfully achieved.

  View details for DOI 10.21037/atm-20-1154

  View details for Web of Science ID 000590231200015

  View details for PubMedID 33313155

  View details for PubMedCentralID PMC7723533

• Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging. JAMA network open Yu, Y., Xie, Y., Thamm, T., Gong, E., Ouyang, J., Huang, C., Christensen, S., Marks, M. P., Lansberg, M. G., Albers, G. W., Zaharchuk, G. 2020; 3 (3): e200772

  Abstract

  Predicting infarct size and location is important for decision-making and prognosis in patients with acute stroke.To determine whether a deep learning model can predict final infarct lesions using magnetic resonance images (MRIs) acquired at initial presentation (baseline) and to compare the model with current clinical prediction methods.In this multicenter prognostic study, a specific type of neural network for image segmentation (U-net) was trained, validated, and tested using patients from the Imaging Collaterals in Acute Stroke (iCAS) study from April 14, 2014, to April 15, 2018, and the Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2) study from July 14, 2008, to September 17, 2011 (reported in October 2012). Patients underwent baseline perfusion-weighted and diffusion-weighted imaging and MRI at 3 to 7 days after baseline. Patients were grouped into unknown, minimal, partial, and major reperfusion status based on 24-hour imaging results. Baseline images acquired at presentation were inputs, and the final true infarct lesion at 3 to 7 days was considered the ground truth for the model. The model calculated the probability of infarction for every voxel, which can be thresholded to produce a prediction. Data were analyzed from July 1, 2018, to March 7, 2019.Area under the curve, Dice score coefficient (DSC) (a metric from 0-1 indicating the extent of overlap between the prediction and the ground truth; a DSC of ?0.5 represents significant overlap), and volume error. Current clinical methods were compared with model performance in subgroups of patients with minimal or major reperfusion.Among the 182 patients included in the model (97 women [53.3%]; mean [SD] age, 65?[16] years), the deep learning model achieved a median area under the curve of 0.92 (interquartile range [IQR], 0.87-0.96), DSC of 0.53 (IQR, 0.31-0.68), and volume error of 9 (IQR, -14 to 29) mL. In subgroups with minimal (DSC, 0.58 [IQR, 0.31-0.67] vs 0.55 [IQR, 0.40-0.65]; P?=?.37) or major (DSC, 0.48 [IQR, 0.29-0.65] vs 0.45 [IQR, 0.15-0.54]; P?=?.002) reperfusion for which comparison with existing clinical methods was possible, the deep learning model had comparable or better performance.The deep learning model appears to have successfully predicted infarct lesions from baseline imaging without reperfusion information and achieved comparable performance to existing clinical methods. Predicting the subacute infarct lesion may help clinicians prepare for decompression treatment and aid in patient selection for neuroprotective clinical trials.

  View details for DOI 10.1001/jamanetworkopen.2020.0772

  View details for PubMedID 32163165

• Absent Contrast Filling of Ipsilateral Superficial Middle Cerebral Vein Predicts Midline Shift in Acute Middle Cerebral Artery Occlusion. Frontiers in neurology Zhang, S., Lin, L., Zhang, R., Wang, M., Yu, Y., Shi, Z., Parsons, M., Geng, Y. 2020; 11: 570844

  Abstract

  Background and purpose: Midline shift is a life-threatening complication of acute large artery occlusion (LAO). The value of superficial middle cerebral vein (SMCV) for predicting midline shift is currently unclear for patients with acute LAO. Methods: Consecutive acute LAO (middle cerebral artery M1 ± intracranial internal carotid artery) patients between March 2018 and May 2019 were included. Absent filling of ipsilateral cortical vein (marked as SMCV-) was defined as no contrast filling into the vein across the whole venous phase of four-dimensional computed tomography (CT) angiography derived from CT perfusion in the ischemic hemisphere. Results: In the total of 81 patients, 31 (38.4%) were identified as SMCV-. SMCV- independently predicted midline shift, with sensitivity of 87.5% and specificity of 82.5%. Receiver operating characteristic analysis showed that including SMCV- as a predictor in addition to baseline ischemic core volume significantly increased the area under the curve in predicting midline shift (SMCV- with baseline ischemic core volume vs. baseline ischemic core volume: AUC = 0.903 vs. 0.841, Z = 2.451, P = 0.014). Conclusion: In acute LAO patients, the presence of SMCV- was a sensitive and specific imaging marker for midline shift. SMCV- had supplementary value to baseline ischemic core volume in predicting midline shift.

  View details for DOI 10.3389/fneur.2020.570844

  View details for PubMedID 33224087

• Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke. Stroke Wang, K., Shou, Q., Ma, S. J., Liebeskind, D., Qiao, X. J., Saver, J., Salamon, N., Kim, H., Yu, Y., Xie, Y., Zaharchuk, G., Scalzo, F., Wang, D. J. 2019: STROKEAHA119027457

  Abstract

  Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)-based algorithm for assisting the selection of suitable patients with acute ischemic stroke for endovascular treatment based on 3-dimensional pseudo-continuous arterial spin labeling (pCASL). Methods- A total of 167 image sets of 3-dimensional pCASL data from 137 patients with acute ischemic stroke scanned on 1.5T and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired dynamic susceptibility contrast magnetic resonance imaging was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke). The trained DL algorithm was further applied on twelve 3-dimensional pCASL data sets acquired on 1.5T and 3T General Electric MR systems, without fine-tuning of parameters. Results- The DL algorithm can predict the dynamic susceptibility contrast-defined hypoperfusion region in pCASL with a voxel-wise area under the curve of 0.958, while the 6 ML algorithms ranged from 0.897 to 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise area under the curve of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. Conclusions- pCASL perfusion magnetic resonance imaging in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.

  View details for DOI 10.1161/STROKEAHA.119.027457

  View details for PubMedID 31884904

• Prediction of Subacute Infarction in Acute Ischemic Stroke Using Baseline Multi-modal MRI and Deep Learning Yu Yannan, Xie, Y., Thamm, T., Chen, K. T., Gong Enhao, Zaharchuk, G. LIPPINCOTT WILLIAMS & WILKINS. 2019

  View details for Web of Science ID 000478733401398

• Quantitative score of the vessel morphology in middle cerebral artery atherosclerosis. Journal of the neurological sciences Meng, Y., Li, M., Yu, Y., Xu, Y., Gao, S., Feng, F., Xu, W. H. 2019; 399: 111?17

  Abstract

  We aimed to quantitatively assess the vessel morphology of middle cerebral artery (MCA) atherosclerosis and explore its value in discriminating plaque types.Patients were selected from a high-resolution magnetic resonance imaging (HRMRI) study from January 2007 to December 2015. One hundred and three patients with acute cerebral infarcts due to MCA stenosis (>50%) and eighty-nine patients with asymptomatic MCA stenosis (>50%) were included. Quantitative measurements of MCA morphology, including lumen area, outer-wall and wall area at stenotic site and reference site, stenotic degree, plaque length, remodeling index and plaque eccentricity, were performed on HRMRI with observers blinded to clinical presentations. Firth's penalized logistic regression analysis was used to construct a symptomatic plaque score (SPS) model. Then, the HRMRI data of 39 patients prospectively enrolled from January 2016 to January 2017 were used to validate the SPS model.The HRMRI data of 103 patients with symptomatic MCA stenosis and 89 patients with asymptomatic MCA stenosis in the construction cohort were analyzed. Four main factors were found to be associated with symptomatic plaques: stenotic lumen area???2.28?mm2, stenotic wall area???8.88?mm2, plaque length and presence of an eccentric plaque. Summation of each logistic regression coefficient multiplying the corresponding score produced the SPS with an area under curve (AUC) of 0.890 on receiver operating characteristics analysis. Validation of the score of 39 plaques (19 symptomatic and 20 asymptomatic) revealed an AUC of 0.862, confirming the continued diagnostic ability. When the data were pooled in all 235 plaques, the optimal cutoff score of discriminating symptomatic and asymptomatic plaques was 2.79 (SPS???2.79 indicating a symptomatic plaque) with AUC?=?0.886, sensitivity 81.1% and specificity 80.5%.The quantitative analysis of MCA morphology can independently and accurately discriminate plaque types, suggesting its close association with the underlying pathophysiology. Further prospective studies are required to verify whether the SPS model is clinically valuable in monitoring plaque progression and assessing the vulnerability.

  View details for DOI 10.1016/j.jns.2019.02.025

  View details for PubMedID 30798108

• LSTM Network for Prediction of Hemorrhagic Transformation in Acute Stroke Yu, Y., Parsi, B., Speier, W., Arnold, C., Lou, M., Scalzo, F., Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P. T., Khan, A. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 177?85

  View details for DOI 10.1007/978-3-030-32251-9_20

  View details for Web of Science ID 000548735900020

• Middle Cerebral Artery Plaque Hyperintensity on T2-Weighted Vessel Wall Imaging Is Associated with Ischemic Stroke. AJNR. American journal of neuroradiology Yu, Y. N., Liu, M. W., Villablanca, J. P., Li, M. L., Xu, Y. Y., Gao, S., Feng, F., Liebeskind, D. S., Scalzo, F., Xu, W. H. 2019

  Abstract

  Vessel wall imaging can identify intracranial atherosclerotic plaque and give clues about its components. We aimed to investigate whether the plaque hyperintensity in the middle cerebral artery on T2-weighted vessel wall imaging is associated with ischemic stroke.We retrospectively reviewed our institutional vessel wall MR imaging data base. Patients with an acute ischemic stroke within 7-day onset in the MCA territory were enrolled. Patients with stroke and stenotic MCA plaque (stenosis degree, ?50%) were included for analysis. Ipsilateral MCA plaque was defined as symptomatic, and contralateral plaque, as asymptomatic. Plaque was manually delineated on T2-weighted vessel wall imaging. The plaque signal was normalized to the ipsilateral muscle signal. The thresholds and volume of normalized plaque signal were investigated using logistic regression and receiver operating characteristic analysis to determine the association between normalized plaque signal and stroke.One hundred eight stenotic MCAs were analyzed (from 88 patients, 66 men; mean age, 58 ± 15 years), including 72 symptomatic and 36 asymptomatic MCA plaques. Symptomatic MCA plaque showed larger plaque hyperintensity volume compared with asymptomatic MCA plaque. The logistic regression model incorporating stenosis degree, remodeling ratio, and normalized plaque signal 1.3-1.4 (OR, 6.25; 95% CI, 1.90-20.57) had a higher area under curve in differentiating symptomatic/asymptomatic MCA plaque, compared with a model with only stenosis degree and remodeling ratio (area under curve, 0.884 versus 0.806; P =.008).The MCA plaque hyperintensity on T2-weighted vessel wall imaging is independently associated with ischemic stroke and adds value to symptomatic MCA plaque classification. Measuring the normalized signal intensity may serve as a practical and integrative approach to the analysis of intracranial atherosclerotic plaque.

  View details for DOI 10.3174/ajnr.A6260

  View details for PubMedID 31624115

• Middle cerebral artery geometric features are associated with plaque distribution and stroke. Neurology Yu, Y. N., Li, M. L., Xu, Y. Y., Meng, Y., Trieu, H., Villablanca, J. P., Gao, S., Feng, F., Liebeskind, D. S., Xu, W. H. 2018; 91 (19): e1760?e1769

  Abstract

  We aimed to investigate the geometric features of the middle cerebral artery (MCA) and their relevance to plaque distribution and ischemic stroke.We reviewed our institutional vessel wall imaging database. Patients with symptomatic MCA atherosclerosis, asymptomatic MCA atherosclerosis, or without MCA atherosclerosis were included. The MCA geometric features, including M1 segment shape and M1 curve orientation, were defined on magnetic resonance angiography. Plaque distribution and other plaque parameters were identified on vessel wall imaging. The association among MCA geometric features, plaque distribution, and ischemic stroke were analyzed.A total of 977 MCAs were analyzed (87 atherosclerotic symptomatic MCAs, 459 atherosclerotic asymptomatic MCAs, and 431 plaque-free MCAs). Overall, curved M1 segments were the predominant shape across all groups. In 91.1% of curved atherosclerotic MCAs, the plaque involved the inner wall of the curve. Plaque not involving the inner wall was shorter (p < 0.0001) and thinner (p = 0.005) compared to plaque involving the inner wall. Inferior plaque was observed in 39.9% of inferior-oriented M1 curves compared to 21.7% in non-inferior-oriented M1 curves (p < 0.0001). The absence of an inferior-oriented M1 curve (odds ratio 0.45, 95% confidence interval 0.27-0.77) and presence of superior plaque (odds ratio 2.67, 95% confidence interval 1.52-4.67) were independently associated with stroke after adjusting for plaque length and thickness, degree of stenosis, and remodeling ratio.MCA geometric features are associated with plaque distribution and stroke. Our findings provide insight into the vascular pathophysiology of MCA atherosclerosis.

  View details for DOI 10.1212/WNL.0000000000006468

  View details for PubMedID 30291186

• Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI. IEEE transactions on bio-medical engineering Yu, Y., Guo, D., Lou, M., Liebeskind, D., Scalzo, F. 2018; 65 (9): 2058?65

  Abstract

  Hemorrhagic transformation (HT) is the most severe complication of reperfusion therapy in acute ischemic stroke (AIS) patients. Management of AIS patients could benefit from accurate prediction of upcoming HT. While prediction of HT occurrence has recently provided encouraging results, the prediction of the severity and territory of the HT could bring valuable insights that are beyond current methods.This study tackles these issues and aims to predict the spatial occurrence of HT in AIS from perfusion-weighted magnetic resonance imaging (PWI) combined with diffusion weighted imaging. In all, 165 patients were included in this study and analyzed retrospectively from a cohort of AIS patients treated with reperfusion therapy in a single stroke center.Machine learning models are compared within our framework; support vector machines, linear regression, decision trees, neural networks, and kernel spectral regression were applied to the dataset. Kernel spectral regression performed best with an accuracy of $\text{83.7} \pm \text{2.6}\%$.The key contribution of our framework formalize HT prediction as a machine learning problem. Specifically, the model learns to extract imaging markers of HT directly from source PWI images rather than from pre-established metrics.Predictions visualized in terms of spatial likelihood of HT in various territories of the brain were evaluated against follow-up gradient recalled echo and provide novel insights for neurointerventionalists prior to endovascular therapy.

  View details for DOI 10.1109/TBME.2017.2783241

  View details for PubMedID 29989941

• Association of anemia and hemoglobin decrease during acute stroke treatment with infarct growth and clinical outcome. PloS one Bellwald, S., Balasubramaniam, R., Nagler, M., Burri, M. S., Fischer, S. D., Hakim, A., Dobrocky, T., Yu, Y., Scalzo, F., Heldner, M. R., Wiest, R., Mono, M. L., Sarikya, H., El-Koussy, M., Mordasini, P., Fischer, U., Schroth, G., Gralla, J., Mattle, H. P., Arnold, M., Liebeskind, D., Jung, S. 2018; 13 (9): e0203535

  Abstract

  Anemia is associated with worse outcome in stroke, but the impact of anemia with intravenous thrombolysis or endovascular therapy has hardly been delineated. The aim of this study was to analyze the role of anemia on infarct evolution and outcome after acute stroke treatment.1158 patients from Bern and 321 from Los Angeles were included. Baseline data and 3 months outcome assessed with the modified Rankin Scale were recorded prospectively. Baseline DWI lesion volumes were measured in 345 patients and both baseline and final infarct volumes in 180 patients using CT or MRI. Multivariable and linear regression analysis were used to determine predictors of outcome and infarct growth.712 patients underwent endovascular treatment and 446 intravenous thrombolysis. Lower hemoglobin at baseline, at 24h, and nadir until day 5 predicted poor outcome (OR 1.150-1.279) and higher mortality (OR 1.131-1.237) independently of treatment. Decrease of hemoglobin after hospital arrival, mainly induced by hemodilution, predicted poor outcome and had a linear association with final infarct volumes and the amount and velocity of infarct growth. Infarcts of patients with newly observed anemia were twice as large as infarcts with normal hemoglobin levels.Anemia at hospital admission and any hemoglobin decrease during acute stroke treatment affect outcome negatively, probably by enlarging and accelerating infarct growth. Our results indicate that hemodilution has an adverse effect on penumbral evolution. Whether hemoglobin decrease in acute stroke could be avoided and whether this would improve outcome would need to be studied prospectively.

  View details for DOI 10.1371/journal.pone.0203535

  View details for PubMedID 30256814

  View details for PubMedCentralID PMC6157859

• Cerebral venous collaterals: A new fort for fighting ischemic stroke? Progress in neurobiology Tong, L. S., Guo, Z. N., Ou, Y. B., Yu, Y. N., Zhang, X. C., Tang, J., Zhang, J. H., Lou, M. 2017; 163-164: 172?93

  Abstract

  Stroke therapy has entered a new era highlighted by the use of endovascular therapy in addition to intravenous thrombolysis. However, the efficacy of current therapeutic regimens might be reduced by their associated adverse events. For example, over-reperfusion and futile recanalization may lead to large infarct, brain swelling, hemorrhagic complication and neurological deterioration. The traditional pathophysiological understanding on ischemic stroke can hardly address these occurrences. Accumulating evidence suggests that a functional cerebral venous drainage, the major blood reservoir and drainage system in brain, may be as critical as arterial infusion for stroke evolution and clinical sequelae. Further exploration of the multi-faceted function of cerebral venous system may add new implications for stroke outcome prediction and future therapeutic decision-making. In this review, we emphasize the anatomical and functional characteristics of the cerebral venous system and illustrate its necessity in facilitating the arterial infusion and maintaining the cerebral perfusion in the pathological stroke content. We then summarize the recent critical clinical studies that underscore the associations between cerebral venous collateral and outcome of ischemic stroke with advanced imaging techniques. A novel three-level venous system classification is proposed to demonstrate the distinct characteristics of venous collaterals in the setting of ischemic stroke. Finally, we discuss the current directions for assessment of cerebral venous collaterals and provide future challenges and opportunities for therapeutic strategies in the light of these new concepts.

  View details for DOI 10.1016/j.pneurobio.2017.11.001

  View details for PubMedID 29199136

  View details for PubMedCentralID PMC6389333

• Different risk factors for poor outcome between patients with positive and negative susceptibility vessel sign. Journal of neurointerventional surgery Yan, S., Liu, K., Tong, L., Yu, Y., Zhang, S., Lou, M. 2016; 8 (10): 1001?5

  Abstract

  The absence of the susceptibility vessel sign (negative SVS) on gradient-recalled echo or susceptibility-weighted imaging (SWI) in thrombolytic therapy has not been well studied. Since positive and negative SVS may have different components, we aimed to investigate the difference in risk factors for clinical outcome between patients with positive and negative SVS.We retrospectively examined clinical and imaging data from 85 consecutive patients with acute ischemic stroke with middle cerebral artery occlusion who underwent SWI before intravenous thrombolysis (IVT). We then examined the predictors of negative SVS and the risk factors for a poor outcome (defined as modified Rankin Scale score ?3) 3?months after IVT in subgroup analysis.Multivariate regression analysis indicated that previous antiplatelet use (OR 0.076; 95% CI 0.007 to 0.847; p=0.036) and shorter time from onset to treatment (OR 1.051; 95% CI 1.003 to 1.102; p=0.037) were inversely associated with poor outcome in patients with negative SVS, while higher baseline National Institutes of Health Stroke Scale (NIHSS) score was associated with poor outcome in patients with positive SVS (OR 1.222; 95% CI 1.084 to 1.377; p=0.001).The risk factors for clinical outcome after IVT in patients with negative SVS may differ from those with positive SVS.

  View details for DOI 10.1136/neurintsurg-2015-011999

  View details for PubMedID 26408028

• Defining Core and Penumbra in Ischemic Stroke: A Voxel- and Volume-Based Analysis of Whole Brain CT Perfusion. Scientific reports Yu, Y., Han, Q., Ding, X., Chen, Q., Ye, K., Zhang, S., Yan, S., Campbell, B. C., Parsons, M. W., Wang, S., Lou, M. 2016; 6: 20932

  Abstract

  Whole brain computed tomography perfusion (CTP) has the potential to select eligible patients for reperfusion therapy. We aimed to find the optimal thresholds on baseline CTP for ischemic core and penumbra in acute ischemic stroke. We reviewed patients with acute ischemic stroke in the anterior circulation, who underwent baseline whole brain CTP, followed by intravenous thrombolysis and perfusion imaging at 24 hours. Patients were divided into those with major reperfusion (to define the ischemic core) and minimal reperfusion (to define the extent of penumbra). Receiver operating characteristic (ROC) analysis and volumetric consistency analysis were performed separately to determine the optimal threshold by Youden's Index and mean magnitude of volume difference, respectively. From a series of 103 patients, 22 patients with minimal-reperfusion and 47 with major reperfusion were included. Analysis revealed delay time ? 3 s most accurately defined penumbra (AUC = 0.813; 95% CI, 0.812-0.814, mean magnitude of volume difference = 29.1 ml). The optimal threshold for ischemic core was rCBF ? 30% within delay time ? 3 s (AUC = 0.758; 95% CI, 0.757-0.760, mean magnitude of volume difference = 10.8 ml). In conclusion, delay time ? 3 s and rCBF ? 30% within delay time ? 3 s are the optimal thresholds for penumbra and core, respectively. These results may allow the application of the mismatch on CTP to reperfusion therapy.

  View details for DOI 10.1038/srep20932

  View details for PubMedID 26860196

  View details for PubMedCentralID PMC4748242

• Optimal magnetic resonance perfusion thresholds identifying ischemic penumbra and infarct core: a Chinese population-based study. CNS neuroscience & therapeutics Zhang, S., Tang, H., Yu, Y. N., Yan, S. Q., Parsons, M. W., Lou, M. 2015; 21 (3): 289?95

  Abstract

  To validate whether the optimal magnetic resonance perfusion (MRP) thresholds for ischemic penumbra and infarct core, between voxel and volume-based analysis, are varied greatly among Chinese acute ischemic stroke patients.Acute ischemic stroke patients receiving intravenous thrombolysis within 6 h of onset that obtained acute and 24-h MRP were reviewed. Patients with either no reperfusion (<30% reperfusion at 24 h) or successful reperfusion (>70% reperfusion at 24 h) were enrolled to investigate the ischemic penumbra and infarct core, respectively. The final infarct was assessed on 24-h diffusion-weighted imaging (DWI), which was retrospectively matched to the baseline perfusion-weighted imaging (PWI) images by volume or voxel-based analysis. The optimal thresholds that determined by each approach were compared.From June 2009 to Jan 2014, of 50 patients enrolled, 19 patients achieved no reperfusion, and 20 patients reperfused at 24 h. In patients with no reperfusion, Tmax > 6 seconds was proved of the best agreement with the final infarct in both volumetric analysis (ratio: 1.05, 95% limits of agreement:-0.23 to 2.33, P < 0.001) and voxel-by-voxel analysis (sensitivity: 72.3%, specificity: 74.3%). In patients with reperfusion, rMTT>225% (ratio:2.4, 95% limits of agreement: -6.5 to 11.4, P < 0.001) was found of the best volumetric agreement with the final infarct, while Tmax > 5.6 seconds (sensitivity: 76.8%, specificity: 70.3%) performed most accurately in voxel-based analysis.Among Chinese acute stroke patients, volume of Tmax >6 seconds may precisely target ischemic penumbra tissue as good as voxel-based analysis performed, albeit no concordant MRP parameter is found to accurately predict infarct core because reperfusion occurred within 24 h after thrombolysis fails to restrain the infarct growth.

  View details for DOI 10.1111/cns.12367

  View details for PubMedID 25476071

  View details for PubMedCentralID PMC6495880

• [Thresholds of CT perfusion in predicting ischemic penumbra and infarct core in patients with acute ischemic stroke]. Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences Yu, Y. N., Ding, X. F., Zhang, S., Lou, M. 2014; 43 (1): 7?13

  Abstract

  To determine the optimal parameters and their thresholds on CT perfusion (CTP) to predict the penumbra and core in patients with acute ischemic stroke.The data of 39 thrombolytic candidates with acute cerebral anterior-circulation ischemic stroke admitted in the Second Affiliated Hospital, Zhejiang University School of Medicine from June 2009 to October 2013 were retrospectively reviewed. Patients all underwent CTP at admission and CTP or magnetic resonance perfusion (MRP) 24 h after thrombolysis. Patients were classified as non-reperfusion group (to define the threshold of penumbra, n=10) and reperfusion group (to define the threshold of infarct core, n=21) by reperfusion status. According to the baseline CTP and 24 h imaging, the volumes of threshold-based hypoperfusion lesions and final infarction were calculated. Paired t test, correlation analysis and Bland-Altman plot were performed to assess the optimal thresholds for predicting the penumbra and infarct core.In non-reperfusion group, the best agreement was found between final infarct volume and delay time>3 s (bias 3.3 ml, 95% limits of agreement:-41.7 to 48.3 ml, r=0.933, P<0.001), while in reperfusion group, the best agreement was noted between final infarct volume and rCBF<30% (bias -2.2 ml, 95% limits of agreement:-25.6 to 21.2 ml; r=0.923, P<0.001).Delay time>3 s and rCBF<30% are the optimal thresholds for predicting the penumbra and infarct core on CTP, respectively. These thresholds may be of help to estimate the mismatch status and select eligible patients for thrombolysis.

  View details for PubMedID 24616455

• [Dynamic CT angiography in evaluation of collateral flow and outcome of acute ischemic stroke patients after intravenous thrombolysis]. Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences Chen, W. L., Ding, X. F., Zhang, S., Yu, Y. N., Chen, Z. C., Lou, M. 2014; 43 (1): 14?19

  Abstract

  To evaluate the collateral flow of patients with acute ischemic stroke by dynamic CT angiography (CTA) and to analyze the relationship between collateral flow and outcome after intravenous thrombolysis.We retrospectively analyzed CT perfusion (CTP) imaging of 22 acute ischemic stroke patients with middle cerebral artery (MCA) or internal carotid artery (ICA) occlusion undergoing intravenous thrombolysis, and reconstructed the images for dynamic CTA in the Second Affiliated Hospital, Zhejiang University School of Medicine from June 2009 to October 2013. The total extent and flow speed of collateral flow based on dynamic CTA images of these patients were evaluated. The scores of National Institute of Health stroke scale (NIHSS) in different collateral flows were compared with repeated measuring. The nonparametric Spearman's rank correlation was used to assess the relationship between collateral flow and modified Rankin scale (mRS) at 3 months after thrombolytic therapy.Compared with the poor collateral flow group, patients with good collateral flow had lower NIHSS at 1 month after thrombolysis (4.7±5.0 vs 25.1±15.1, P=0.001) and higher reperfusion percentage (69%±32% vs 23%±54%, P=0.044). The total condition score of collateral flow was positively correlated with mRS at 3 months after treatment (r=0.450, P=0.001).Acute ischemic stroke patients with good collateral flow after intravenous thrombolysis have a better outcome. The dynamic CTA can be used to evaluate the collateral flow and to predict clinical outcomes in patients with acute ischemic stroke after thrombolysis therapy.

  View details for PubMedID 24616456