Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.

OBJECTIVE: To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment. METHODS: A retrospective analysis was conducted on 174 patients who underwent endovascular treatment for acute anterior circulation ischemic stroke between January 2020 and December 2023. FR was defined as successful recanalization but poor prognosis at 90 days (modified Rankin Scale, mRS 4-6). Radiomic features were extracted from non-contrast CT and selected using the least absolute shrinkage and selection operator (LASSO) regression method. Logistic regression (LR) model was used to build models based on radiomic and clinical features. A radiomics-clinical nomogram model was developed, and the predictive performance of the models was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS: A total of 174 patients were included. 2016 radiomic features were extracted from non-contrast CT, and 9 features were selected to build the radiomics model. Univariate and stepwise multivariate analyses identified admission NIHSS score, hemorrhagic transformation, NLR, and admission blood glucose as independent factors for building the clinical model. The AUC of the radiomics-clinical nomogram model in the training and testing cohorts were 0.860 (95%CI 0.801-0.919) and 0.775 (95%CI 0.605-0.945), respectively. CONCLUSION: The radiomics-clinical nomogram model based on non-contrast CT demonstrated satisfactory performance in predicting futile recanalization in patients with anterior circulation acute ischemic stroke.

Deferoxamine in intracerebral hemorrhage: Systematic review and meta-analysis.

BACKGROUND: Intracerebral hemorrhage (ICH) is a stroke with a high morbidity and mortality rate. Deferoxamine (DFX) is thought to be effective in treating Intracerebral Hemorrhage. In our study, we performed a meta-analysis to evaluate the treatment effects of DFX. METHODS: We systematically searched PubMed, Embase, Web of Science, the Cochrane Central Register of Controlled Trials, and Chinese Biomedical Literature Database in Jan 2022 for studies on DFX for ICH patients. Outcome measures included relative hematoma volume, relative edema volume, good neurological functional outcome and adverse events. Odds risk (OR) and weighted mean difference (WMD) were used to evaluate clinical outcomes. RESULTS: After searching 636 articles, 4 RCTs, 2 NRCTs, and 1cohort study were included. We found that DFX was effective in hematoma absorption on day 7 after onset, but the difference was not significant on day 14. DFX could suppress edema expansion on days 3, 7, and 14 after onset. DFX did not contribute to better outcomes after 3 and 6 months when used the modified Rankin Scale and the Glasgow Outcome Scale to evaluate neurological prognosis. The pooled results showed no statistically significant difference in Serious adverse events between the experimental and control groups. CONCLUSIONS: DFX could limit edema expansion on days 3, 7, and 14 after commencement and facilitate hematoma absorption at week 1 without significantly increasing the risk of adverse events, but it did not improve neurological prognosis.