Association of Blood Biomarkers of Inflammation With Penumbra Consumption After Mechanical Thrombectomy in Patients With Acute Ischemic Stroke.

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.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review.

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which "ground truth" data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

Impact of the reperfusion status for predicting the final stroke infarct using deep learning.

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.

Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke.

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.

Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke.

We address the medical image analysis issue of predicting the final lesion in stroke from early perfusion magnetic resonance imaging. The classical processing approach for the dynamical perfusion images consists in a temporal deconvolution to improve the temporal signals associated with each voxel before performing prediction. We demonstrate here the value of exploiting directly the raw perfusion data by encoding the local environment of each voxel as a spatio-temporal texture, with an observation scale larger than the voxel. As a first illustration for this approach, the textures are characterized with local binary patterns and the classification is performed using a standard support vector machine (SVM). This simple machine learning classification scheme demonstrates results with 95% accuracy on average while working only raw perfusion data. We discuss the influence of the observation scale and evaluate the interest of using post-processed perfusion data with this approach.