Brain atrophy in acute ischaemic stroke patients treated with reperfusion therapy: a systematic review

Prevalence and Impact of Cerebral Microbleeds on Clinical and Safety Outcomes in Acute Ischaemic Stroke Patients Receiving Reperfusion Therapy: A Systematic Review and Meta-Analysis

BACKGROUND

Cerebral microbleeds (CMBs), a notable neuroimaging finding often associated with cerebral microangiopathy, demonstrate a heightened prevalence in patients diagnosed with acute ischemic stroke (AIS), which is in turn linked to less favourable clinical prognoses. Nevertheless, the exact prevalence of CMBs and their influence on post-reperfusion therapy outcomes remain inadequately elucidated.

MATERIALS AND METHODS

Through systematic searches of PubMed, Embase and Cochrane databases, studies were identified adhering to specific inclusion criteria: (a) AIS patients, (b) age ≥ 18 years, (c) CMBs at baseline, (d) availability of comparative data between CMB-positive and CMB-negative groups, along with relevant post-reperfusion therapy outcomes. The data extracted were analysed using forest plots of odds ratios, and random-effects modelling was applied to investigate the association between CMBs and symptomatic intracerebral haemorrhage (sICH), haemorrhagic transformation (HT), 90-day functional outcomes, and 90-day mortality post-reperfusion therapy.

RESULTS

In a total cohort of 9776 AIS patients who underwent reperfusion therapy, 1709 had CMBs, with a pooled prevalence of 19% (ES 0.19; 95% CI: 0.16, 0.23, p < 0.001). CMBs significantly increased the odds of sICH (OR 2.57; 95% CI: 1.72; 3.83; p < 0.0001), HT (OR 1.53; 95% CI: 1.25; 1.88; p < 0.0001), as well as poor functional outcomes at 90 days (OR 1.59; 95% CI: 1.34; 1.89; p < 0.0001) and 90-day mortality (OR 1.65; 95% CI: 1.27; 2.16; p < 0.0001), relative to those without CMBs, in AIS patients undergoing reperfusion therapy (encompassing intravenous thrombolysis [IVT], endovascular thrombectomy [EVT], either IVT or EVT, and bridging therapy). Variations in the level of association can be observed among different subgroups of reperfusion therapy.

CONCLUSIONS

This meta-analysis underscores a significant association between CMBs and adverse postprocedural safety outcomes encompassing sICH, HT, poor functional outcome, and increased mortality in AIS patients undergoing reperfusion therapy. The notable prevalence of CMBs in both the overall AIS population and those undergoing reperfusion therapy emphasizes their importance in post-stroke prognostication.

Prognostic accuracy and impact of cerebral collateral status on clinical and safety outcomes in acute ischemic stroke patients receiving reperfusion therapy: a systematic meta-analysis

BACKGROUND

Cerebral collateral status has a potential role in mediating postreperfusion clinical and safety outcomes in acute ischemic stroke (AIS).

PURPOSE

To investigate the prognostic accuracy and impact of collateral status on clinical and safety outcomes in patients with AIS receiving reperfusion therapy.

MATERIAL AND METHODS

Studies with AIS patients treated with reperfusion therapy, collateral status assessed using Tan, ASITN/SIR, or similar collateral grading methods and data stratified according to collateral status were included. Relevant data on clinical outcomes, such as functional outcome at 90 days, mortality at 90 days, angiographic reperfusion, symptomatic intracerebral hemorrhage (sICH) and hemorrhagic transformation (HT), were collated and analyzed.

RESULTS

A meta-analysis of 18 studies involving 4132 patients with AIS was conducted. Good collateral status was significantly associated with angiographic reperfusion (odds ratio [OR]=1.97, 95% confidence interval [CI]=1.38-2.80; P < 0.0001), sICH (OR=0.67, 95% CI=0.46-0.99; P = 0.042), and 90-day functional outcome (OR=3.05, 95% CI=1.78-5.24; P < 0.0001). However, its association with HT (OR=0.76, 95% CI=0.38-1.51; P = 0.425) and three-month mortality (OR=0.53, 95% CI=0.17-1.69; P = 0.280) did not reach statistical significance. The prognostic accuracy of collaterals for predicting angiographic reperfusion, HT, functional outcome (at 90 days), and mortality (at 90 days) were 63%, 49%, 66%, and 48%, respectively.

CONCLUSION

Cerebral collaterals are significantly associated with clinical and safety outcomes, albeit with a prognostic accuracy range of 48%-66%; thus, evaluation of their patency is a useful prognostic tool in patients with AIS receiving reperfusion therapy.

Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models

Objective

Brain atrophy is an important imaging characteristic of cerebral small vascular disease (CSVD). Our study explores the linear measurement application on CT images of CSVD patients and develops a fully automatic brain atrophy classification model. The second aim was to compare it with the end-to-end Convolutional Neural Networks (CNNs) model.

Methods

A total of 385 subjects such as 107 no-atrophy brain, 185 mild atrophy, and 93 severe atrophy were collected and randomly separated into training set (n = 308) and test set (n = 77). Key slices for linear measurement were manually identified and used to annotate nine linear measurements and a binary classification of cerebral sulci widening. A linear-measurement-based pipeline (2D model) was constructed for two-types (existence/non-existence brain atrophy) or three-types classification (no/mild atrophy/severe atrophy). For comparison, an end-to-end CNN model (3D-deep learning model) for brain atrophy classification was also developed. Furthermore, age and gender were integrated to the 2D and 3D models. The sensitivity, specificity, accuracy, average F1 score, receiver operating characteristics (ROC) curves for two-type classification and weighed kappa for three-type classification of the two models were compared.

Results

Automated measurement of linear measurements and cerebral sulci widening achieved moderate to almost perfect agreement with manual annotation. In two-type atrophy classification, area under the curves (AUCs) of the 2D model and 3D model were 0.953 and 0.941 with no significant difference (p = 0.250). The Weighted kappa of the 2D model and 3D model were 0.727 and 0.607 according to standard classification they displayed, mild atrophy and severe atrophy, respectively. Applying patient age and gender information improved classification performances of both 2D and 3D models in two-type and three-type classification of brain atrophy.

Conclusion

We provide a model composed of different modules that can classify CSVD-related brain atrophy on CT images automatically, using linear measurement. It has similar performance and better interpretability than the end-to-end CNNs model and may prove advantageous in the clinical setting.