Search

Background and Objectives: The objective of this study was to assess the relationship between blood biomarkers of inflammation and lesion growth within the penumbra in acute ischemic stroke (AIS) patients treated with mechanical thrombectomy (MT)., Methods: The HIBISCUS-STROKE cohort enrolled patients admitted in the Lyon Stroke Center for an anterior circulation AIS treated with MT after brain MRI assessment. Lesion growth within the penumbra was assessed on day 6 MRI using a voxel-based nonlinear coregistration method and dichotomized into low and high according to the median value. C-reactive protein, interleukin (IL)-6, IL-8, IL-10, monocyte chemoattractant protein-1, soluble tumor necrosis factor receptor I, soluble form suppression of tumorigenicity 2 (sST2), soluble P-selectin, vascular cellular adhesion molecule-1, and matrix metalloproteinase-9 were measured in sera at 4 time points within the first 48 hours. Reperfusion was considered as successful if Thrombolysis in Cerebral Infarction score was 2b/2c/3. A multiple logistic regression model was performed to detect any association between area under the curve (AUC) of these biomarkers within the first 48 hours and a high lesion growth within the penumbra., Results: Ninety patients were included. The median lesion growth within the penumbra was 2.3 (0.7-6.2) mL. On multivariable analysis, a high sST2 AUC (OR 3.77, 95% CI 1.36-10.46), a high baseline DWI volume (OR 3.65, 95% CI 1.32-10.12), and a lack of successful reperfusion (OR 0.19, 95% CI 0.04-0.92) were associated with a high lesion growth within the penumbra. When restricting analyses to patients with successful reperfusion (n = 76), a high sST2 AUC (OR 5.03, 95% CI 1.64-15.40), a high baseline DWI volume (OR 3.74, 95% CI 1.22-11.53), and a high penumbra volume (OR 3.25, 95% CI 1.10-9.57) remained associated with a high lesion growth within the penumbra., Discussion: High sST2 levels within the first 48 hours are associated with a high lesion growth within the penumbra., (© 2022 American Academy of Neurology.)

Background: Predictive maps of the final infarct may help therapeutic decisions in acute ischemic stroke patients. Our objectives were to assess whether integrating the reperfusion status into deep learning models would improve their performance, and to compare them to current clinical prediction methods., Methods: We trained and tested convolutional neural networks (CNNs) to predict the final infarct in acute ischemic stroke patients treated by thrombectomy in our center. When training the CNNs, non-reperfused patients from a non-thrombectomized cohort were added to the training set to increase the size of this group. Baseline diffusion and perfusion-weighted magnetic resonance imaging (MRI) were used as inputs, and the lesion segmented on day-6 MRI served as the ground truth for the final infarct. The cohort was dichotomized into two subsets, reperfused and non-reperfused patients, from which reperfusion status specific CNNs were developed and compared to one another, and to the clinically-used perfusion-diffusion mismatch model. Evaluation metrics included the Dice similarity coefficient (DSC), precision, recall, volumetric similarity, Hausdorff distance and area-under-the-curve (AUC)., Results: We analyzed 109 patients, including 35 without reperfusion. The highest DSC were achieved in both reperfused and non-reperfused patients (DSC = 0.44 ± 0.25 and 0.47 ± 0.17, respectively) when using the corresponding reperfusion status-specific CNN. CNN-based models achieved higher DSC and AUC values compared to those of perfusion-diffusion mismatch models (reperfused patients: AUC = 0.87 ± 0.13 vs 0.79 ± 0.17, P < 0.001; non-reperfused patients: AUC = 0.81 ± 0.13 vs 0.73 ± 0.14, P < 0.01, in CNN vs perfusion-diffusion mismatch models, respectively)., Conclusion: The performance of deep learning models improved when the reperfusion status was incorporated in their training. CNN-based models outperformed the clinically-used perfusion-diffusion mismatch model. Comparing the predicted infarct in case of successful vs failed reperfusion may help in estimating the treatment effect and guiding therapeutic decisions in selected patients., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The problem of final tissue outcome prediction of acute ischemic stroke is assessed from physically realistic simulated perfusion magnetic resonance images. Different types of simulations with a focus on the arterial input function are discussed. These simulated perfusion magnetic resonance images are fed to convolutional neural network to predict real patients. Performances close to the state-of-the-art performances are obtained with a patient specific approach. This approach consists in training a model only from simulated images tuned to the arterial input function of a tested real patient. This demonstrates the added value of physically realistic simulated images to predict the final infarct from perfusion., (Copyright © 2019 Elsevier Ltd. All rights reserved.)