The Value of Susceptibility-Weighted Imaging (SWI) in Evaluating the Ischemic Penumbra of Patients with Acute Cerebral Ischemic Stroke

Imaging of Suspected Stroke in Children, From the AJR Special Series on Emergency Radiology

Pediatric stroke encompasses different causes, clinical presentations, and associated conditions across ages. Although it is relatively uncommon, pediatric stroke presents with poor short- and long-term outcomes in many cases. Because of a wide range of overlapping presenting symptoms between pediatric stroke and other more common conditions, such as migraine and seizures, stroke diagnosis can be challenging or delayed in children. When combined with a comprehensive medical history and physical examination, neuroimaging plays a crucial role in diagnosing stroke and differentiating stroke mimics. This review highlights the current neuroimaging workup for diagnosing pediatric stroke in the emergency department, describes advantages and disadvantages of different imaging modalities, highlights disorders that predispose children to infarct or hemorrhage, and presents an overview of stroke mimics. Key differences in the initial approach to suspected stroke between children and adults are also discussed.

Radiomics features of DSC-PWI in time dimension may provide a new chance to identify ischemic stroke

Ischemic stroke has become a severe disease endangering human life. However, few studies have analyzed the radiomics features that are of great clinical significance for the diagnosis, treatment, and prognosis of patients with ischemic stroke. Due to sufficient cerebral blood flow information in dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) images, this study aims to find the critical features hidden in DSC-PWI images to characterize hypoperfusion areas (HA) and normal areas (NA). This study retrospectively analyzed 80 DSC-PWI data of 56 patients with ischemic stroke from 2013 to 2016. For exploring features in HA and NA,13 feature sets (Fmethod) were obtained from different feature selection algorithms. Furthermore, these 13 Fmethod were validated in identifying HA and NA and distinguishing the proportion of ischemic lesions in brain tissue. In identifying HA and NA, the composite score (CS) of the 13 Fmethod ranged from 0.624 to 0.925. FLasso in the 13 Fmethod achieved the best performance with mAcc of 0.958, mPre of 0.96, mAuc of 0.982, mF1 of 0.959, and mRecall of 0.96. As to classifying the proportion of the ischemic region, the best CS was 0.786, with Acc of 0.888 and Pre of 0.863. The classification ability was relatively stable when the reference threshold (RT) was <0.25. Otherwise, when RT was >0.25, the performance will gradually decrease as its increases. These results showed that radiomics features extracted from the Lasso algorithms could accurately reflect cerebral blood flow changes and classify HA and NA. Besides, In the event of ischemic stroke, the ability of radiomics features to distinguish the proportion of ischemic areas needs to be improved. Further research should be conducted on feature engineering, model optimization, and the universality of the algorithms in the future.

A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke

Background: The ability to accurately detect ischemic stroke and predict its neurological recovery is of great clinical value. This study intended to evaluate the performance of whole-brain dynamic radiomics features (DRF) for ischemic stroke detection, neurological impairment assessment, and outcome prediction. Methods: The supervised feature selection (Lasso) and unsupervised feature-selection methods (five-feature dimension-reduction algorithms) were used to generate four experimental groups with DRF in different combinations. Ten machine learning models were used to evaluate their performance by ten-fold cross-validation. Results: In experimental group_A, the best AUCs (0.873 for stroke detection, 0.795 for NIHSS assessment, and 0.818 for outcome prediction) were obtained by outstanding DRF selected by Lasso, and the performance of significant DRF was better than the five-feature dimension-reduction algorithms. The selected outstanding dimension-reduction DRF in experimental group_C obtained a better AUC than dimension-reduction DRF in experimental group_A but were inferior to the outstanding DRF in experimental group_A. When combining the outstanding DRF with each dimension-reduction DRF (experimental group_B), the performance can be improved in ischemic stroke detection (best AUC = 0.899) and NIHSS assessment (best AUC = 0.835) but failed in outcome prediction (best AUC = 0.806). The performance can be further improved when combining outstanding DRF with outstanding dimension-reduction DRF (experimental group_D), achieving the highest AUC scores in all three evaluation items (0.925 for stroke detection, 0.853 for NIHSS assessment, and 0.828 for outcome prediction). By the method in this study, comparing the best AUC of Ft-test in experimental group_A and the best_AUC in experimental group_D, the AUC in stroke detection increased by 19.4% (from 0.731 to 0.925), the AUC in NIHSS assessment increased by 20.1% (from 0.652 to 0.853), and the AUC in prognosis prediction increased by 14.9% (from 0.679 to 0.828). This study provided a potential clinical tool for detailed clinical diagnosis and outcome prediction before treatment.

Integrated Medical Care and the Continuous 4C Nursing Model to Improve Nursing Quality and Clinical Treatment of Patients with Acute Stroke: Based on a Retrospective Case-Control Study

Objective

This research paper is based on a retrospective case-control study for exploring the effects of medical nursing integration and the continuous 4C nursing model to improve the clinical treatment and nursing quality of patients with acute stroke.

Method

For this purpose, a total of 313 patients with acute stroke, treated in our hospital from February 2020 to April 2021, were enrolled. They were divided into control and study groups with an even number of patients. The control group received integrated medical care number (N = 156), while the study group received integrated medical care and a continuous 4C nursing model (N = 157). In integrated medical care, the general data, self-nursing ability, degree of neurological impairment, Fugl–Meyer Assessment (FMA) score, Barthel index score, and quality of life score were compared between the two groups.

Result

The self-nursing concept, self-nursing responsibility, self-nursing skills, health knowledge, and total score of the patients in the study group were higher than those in the control group (P < 0.05). The neurological function scores of the study group were lower than those of the control group at 1, 3, and 6 months after discharge (P < 0.05). The scores of the study group were higher than those of the control group at 1, 3, and 6 months after discharge (P < 0.05). The Barthel index score of the study group was higher than that of the control group at 1, 3, and 6 months after discharge. The scores of physical function, psychological function, social function, and health self-cognition in the study group were lower than those in the control group (P < 0.05).

Conclusion

The application of integrated medical care and the continuous 4C nursing model for patients with acute stroke is beneficial to enhance the degree of neurological impairment of stroke patients, improve activities of daily life and motor function, and facilitate patients' quality of life. It is helpful to strengthen the attitude and feeling of cooperation between doctors and nurses, promote cooperation between doctors and nurses, reduce the defects of nursing work, heighten the quality of nursing, and achieve the requirement and goal of effectively promoting high-quality nursing.

Deep Learning-Based Diffusion-Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction

The prefiltered image was imported into the local higher-order singular value decomposition (HOSVD) denoising algorithm (GL-HOSVD)-optimized diffusion-weighted imaging (DWI) image, which was compared with the deviation correction nonlocal mean (NL mean) and low-level edge algorithm (LR + edge). Regarding the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), sensitivity, specificity, accuracy, and consistency, the application effect of the GL-HOSVD algorithm in DWI was investigated, and its adoption effect in the examination of ischemic penumbra (IP) of early acute cerebral infarction (ACI) patients was evaluated. A total of 210 patients with ACI were selected as the research subjects, who were randomly rolled into two groups. Those who were checked by conventional DWI were set as the control group, and those who used DWI based on the GL-HOSVD denoising algorithm were set as the observation group, with 105 people in each. Positron emission tomography (PET) test results were set as the gold standard to evaluate the application value of the two examination methods. It was found that under different noise levels, the peak signal-to-noise ratio (PSNR) of the GL-HOSVD algorithm and the root mean square error (RMSE) of the FA parameter were better than those of the nonlocal means (NL-means) of deviation correction and low-rank edge algorithm (LR + edge). The sensitivity, specificity, accuracy, and consistency (8.76%, 81.25%, 87.62%, and 0.52) of the observation group were higher than those of the control group (57.78%, 53.33%, 57.14%, and 0.35) (P < 0.05). Moreover, the apparent diffusion coefficient (ADC) of the DWI images of the observation group was basically consistent with that of the PET images, while the control group had a poor display effect and low definition. In summary, under different noise levels, the GL-HOSVD algorithm had a good denoising effect and greatly reduced fringe artifacts. DWI after denoising had high sensitivity, specificity, accuracy, and consistency in the detection of IP, which was worthy of clinical application and promotion.

SWI as a promising tool comparable to CT perfusion in evaluation of acute cerebral infarction

Background

The recent advances in magnetic resonance imaging techniques have improved the assessment of acute stroke. Susceptibility weighted imaging (SWI) has a crucial role in the management plan of cerebral ischemia. This study was aimed to assess the role of susceptibility-weighted imaging in assessment of area at risk (pneumbra) compared to CT perfusion in patients with acute ischemic infraction.

Results

We found the mean aspect score for SWI 4 ± 1.4 and mean aspect for DWI 7.6 ± 1.2; in addition, mean aspect for CTP was 4.6 ± 1.3. Significant difference is noted between the SWI and DWI with significant p value. But there is no significant difference between the SWI and CTP ASPECT scores.

Conclusion

SWI is a promising technique and comparable to CT perfusion is evaluation of penumbra in the settings of acute infarction.