Blood-brain barrier breakdown in brain ischemia: Insights from MRI perfusion imaging

Brain ischemia is a major cause of neurological dysfunction and mortality worldwide. It occurs not only acutely, such as in acute ischemic stroke (AIS), but also in chronic conditions like cerebral small vessel disease (cSVD). Any other conditions resulting in brain hypoperfusion can also lead to ischemia. Ischemic events can cause blood-brain barrier (BBB) disruption and, ultimately, white matter alterations, contributing to neurological deficits and long-term functional impairments. Hence, understanding the mechanisms of BBB breakdown and white matter injury across various ischemic conditions is critical for developing effective interventions and improving patient outcomes. This review discusses the proposed mechanisms of ischemia-related BBB breakdown. Moreover, magnetic resonance imaging (MRI) based perfusion-weighted imaging (PWI) techniques sensitive to BBB permeability changes are described, including dynamic contrast-enhanced (DCE-MRI) and dynamic susceptibility contrast MRI (DSC-MRI), two perfusion-weighted imaging (PWI). These PWI techniques provide valuable insights that improve our understanding of the complex early pathophysiology of brain ischemia, which can lead to better assessment and management. Finally, in this review, we explore the implications of the mentioned neuroimaging findings, which emphasize the potential of neuroimaging biomarkers to guide personalized treatment and inform novel neuroprotective strategies. This review highlights the importance of investigating BBB changes in brain ischemia and the critical role of advanced neuroimaging in improving patient care and advancing stroke research.

Long-term outcome followed for more than 5 years after revascularization surgery for the treatment of atherosclerotic steno-occlusive disease: poor outcome prediction using machine learning and analysis of the results

Cerebral revascularization for the treatment of atherosclerotic steno-occlusive disease (ASOD) was found to have no benefit compared with medical treatment. However, there is also criticism that with sufficiently long-term follow-up, a crossover might emerge demonstrating the advantages of surgery. Therefore, we examined the long-term outcome of cerebral revascularization performed on patients with carefully selected ASOD at our center. Patients undergoing bypass surgery for non-moyamoya ischemic disease were retrospectively identified. The inclusion criteria were symptomatic ASOD with hemodynamic insufficiency, follow-up of more than 5 years, and stroke or surgical complications during follow-up. The clinical course and radiological findings were investigated. Poor outcomes were predicted using machine learning (ML) models, and Shapley additive explanation (SHAP) values and feature importance of each model were analyzed. A total of 109 patients were included from 2007 to 2018. The 30-day risk of any stroke or death was 6.4% (7/109). The risk of ipsilateral ischemic stroke during median follow-up of 116 months was 7.3% (8/109). The SHAP values showed that previously and empirically known stroke risk factors exert a relatively consistent effect on the prediction of models. The number of lesions with stenosis > 50% (odds ratio [OR] 5.77), age (OR 1.13), and coronary artery disease (OR 5.73) were consistent risk factors for poor outcome. We demonstrated an acceptable long-term outcome of cerebral revascularization surgery for patients with hemodynamically insufficient and symptomatic ASOD. Multicenter studies are encouraged to predict poor outcomes and suitable patients with large numbers of quantitative and qualitative data.

Multi-modality multi-task model for mRS prediction using diffusion-weighted resonance imaging

This study focuses on predicting the prognosis of acute ischemic stroke patients with focal neurologic symptoms using a combination of diffusion-weighted magnetic resonance imaging (DWI) and clinical information. The primary outcome is a poor functional outcome defined by a modified Rankin Scale (mRS) score of 3-6 after 3 months of stroke. Employing nnUnet for DWI lesion segmentation, the study utilizes both multi-task and multi-modality methodologies, integrating DWI and clinical data for prognosis prediction. Integrating the two modalities was shown to improve performance by 0.04 compared to using DWI only. The model achieves notable performance metrics, with a dice score of 0.7375 for lesion segmentation and an area under the curve of 0.8080 for mRS prediction. These results surpass existing scoring systems, showing a 0.16 improvement over the Totaled Health Risks in Vascular Events score. The study further employs grad-class activation maps to identify critical brain regions influencing mRS scores. Analysis of the feature map reveals the efficacy of the multi-tasking nnUnet in predicting poor outcomes, providing insights into the interplay between DWI and clinical data. In conclusion, the integrated approach demonstrates significant advancements in prognosis prediction for cerebral infarction patients, offering a superior alternative to current scoring systems.

Cerebral Hypoxia: Its Role in Age-Related Chronic and Acute Cognitive Dysfunction

Postoperative cognitive dysfunction (POCD) has been reported with widely varying frequency but appears to be strongly associated with aging. Outside of the surgical arena, chronic and acute cerebral hypoxia may exist as a result of respiratory, cardiovascular, or anemic conditions. Hypoxia has been extensively implicated in cognitive impairment. Furthermore, disease states associated with hypoxia both accompany and progress with aging. Perioperative cerebral hypoxia is likely underdiagnosed, and its contribution to POCD is underappreciated. Herein, we discuss the various disease processes and forms in which hypoxia may contribute to POCD. Furthermore, we outline hypoxia-related mechanisms, such as hypoxia-inducible factor activation, cerebral ischemia, cerebrovascular reserve, excitotoxicity, and neuroinflammation, which may contribute to cognitive impairment and how these mechanisms interact with aging. Finally, we discuss opportunities to prevent and manage POCD related to hypoxia.

Comparison of Diagnostic Effects of T2-Weighted Imaging, DWI, SWI, and DTI in Acute Cerebral Infarction

Objective: To achieve precision medicine, the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and improvement of the prognosis of patients.Methods: In this work, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), and diffusion tensor imaging (DTI) examinations were performed on 34 patients with clinically diagnosed cerebral infarction to measure the difference in signal intensity between the lesion and its mirror area and make a comparative analysis by means of the Student-Newman-Keuls method.Results: The detection rate of T2WI was 79% (27/34), the detection rate of DWI was 97% (33/34), the detection rate of SWI was 88% (30/34), and the detection rate of DTI was 94% (32/34).Conclusion: The imaging performance was in the order DWI > DTI > SWI > T2WI for the diagnosis of cerebral infarction, and combined imaging is better than single imaging.

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.