The Etiology of Intracranial Artery Stenosis in Autoimmune Rheumatic Diseases-An Observational High-Resolution Magnetic Resonance Imaging Study.

BACKGROUND AND PURPOSE: Autoimmune rheumatic diseases (AIRD) can cause intracranial artery stenosis (ICAS) and lead to stroke. This study aimed to characterize patients with ICAS associated with AIRD. MATERIALS AND METHODS: Utilizing data from a high-resolution magnetic resonance imaging (HRMRI) database, we retrospectively reviewed AIRD patients with ICAS. Stratification into vasculitis, atherosclerosis, and mixed athero-vasculitis subtypes was based on imaging findings, followed by a comparative analysis of clinical characteristics and outcomes across these subgroups. RESULTS: Among 139 patients (45.1±17.3 years; 64.7% females), 56 (40.3%) were identified with vasculitis, 57 (41.0%) with atherosclerosis, and 26 (18.7%) with mixed athero-vasculitis. The average interval from AIRD-onset to HRMRI was 5 years. Patients with vasculitis presented with a younger age of AIRD-onset (34.5±19.4 years), nearly ten years earlier than other groups (P=0.010), with a higher artery occlusion incidence (44.6% vs. 21.1% and 26.9%, P=0.021). Patients with atherosclerosis showed the highest cardiovascular risk factor prevalence (73.7% vs. 48.2% and 61.5%, P=0.021) but lower intracranial artery wall enhancement instances (63.2% vs. 100% in others, P<0.001). The mixed athero-vasculitis group, predominantly male (69.2% vs. 30.4% and 25.6%, P<0.001), exhibited the most arterial involvement (5 arteries per person vs. 3 and 2, P=0.001). Over an average 21-month follow-up, 23 (17.0%) patients experienced stroke events, and 8 (5.9%) died, with the mixed athero-vasculitis group facing the highest risk of stroke events (32.0%) and the highest mortality (12.0%). CONCLUSIONS: Intracranial arteries are injured and lead to heterogeneous disease courses when exposed to AIRD and cardiovascular risk factors. While atherosclerosis acceleration is common, vasculitis may further contribute to early-developed occlusion and multiple artery involvement. Varied intracranial arteriopathies may result in different outcomes. ABBREVIATIONS: ICAS = intracranial artery stenosis; AIRD = Autoimmune rheumatic diseases; HRMRI = high-resolution magnetic resonance imaging.

PCSK9 Inhibitor with Statin Therapy for Intracranial Artery Stenosis ( PISTIAS): Rationale and design of a multicenter randomized controlled trial.

RATIONALE: Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors enable an additional 54-75% reduction in low-density lipoprotein cholesterol (LDL-C) in statin-treated patients, demonstrating plaque regression in coronary artery disease. However, the impact of achieving an extremely low level of LDL-C with PCSK9 inhibitors (e.g. Evolocumab) on symptomatic intracranial atherosclerosis remains unexplored. AIM AND HYPOTHESIS: To determine whether combining Evolocumab and statins achieves a more significant symptomatic intracranial plaque regression than statin therapy alone. SAMPLE SIZE ESTIMATES: With a sample size of 1000 subjects, a two-sided α of 0.05, and 20% lost to follow-up, the study will have 83.3% power to detect the difference in intracranial plaque burden. METHODS AND DESIGN: This is an investigator-initiated multicenter, randomized, open-label, outcome assessor-blinded trial, evaluating the impact of combining Evolocumab and statins on intracranial plaque burden assessed by high-resolution magnetic resonance imaging at baseline in patients undergoing a clinically indicated acute stroke or transient ischemic attack due to intracranial artery stenosis, and after 24 weeks of treatment. Subjects (n = 1000) were randomized 1:1 into two groups to receive either Evolocumab 140 mg every 2 weeks with statin therapy or statin therapy alone. STUDY OUTCOMES: The primary endpoint is the change in intracranial plaque burden assessed by high-resolution magnetic resonance imaging, performed at baseline and at the end of the 24-week treatment period. DISCUSSION: This trial will explore whether more significant intracranial plaque regression is achievable with the treatment of combining Evolocumab and statins, providing information about efficacy and safety data. TRIAL REGISTRATION NUMBER: ChiCTR2300068868; https://www.chictr.org.cn/.

Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease: A Systematic Review and Meta-Analysis.

Alzheimer's disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150-1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878-1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved. Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].

Radiological biomarkers of idiopathic normal pressure hydrocephalus: new approaches for detecting concomitant Alzheimer's disease and predicting prognosis.

Idiopathic normal pressure hydrocephalus (iNPH) is a clinical syndrome characterized by cognitive decline, gait disturbance, and urinary incontinence. As iNPH often occurs in elderly individuals prone to many types of comorbidity, a differential diagnosis with other neurodegenerative diseases is crucial, especially Alzheimer's disease (AD). A growing body of published work provides evidence of radiological methods, including multimodal magnetic resonance imaging and positron emission tomography, which may help noninvasively differentiate iNPH from AD or reveal concurrent AD pathology in vivo. Imaging methods detecting morphological changes, white matter microstructural changes, cerebrospinal fluid circulation, and molecular imaging have been widely applied in iNPH patients. Here, we review radiological biomarkers using different methods in evaluating iNPH pathophysiology and differentiating or detecting concomitant AD, to noninvasively predict the possible outcome postshunt and select candidates for shunt surgery.

Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease that severely affects the activities of daily living in aged individuals, which typically needs to be diagnosed at an early stage. Generative adversarial networks (GANs) provide a new deep learning method that show good performance in image processing, while it remains to be verified whether a GAN brings benefit in AD diagnosis. The purpose of this research is to systematically review psychoradiological studies on the application of a GAN in the diagnosis of AD from the aspects of classification of AD state and AD-related image processing compared with other methods. In addition, we evaluated the research methodology and provided suggestions from the perspective of clinical application. Compared with other methods, a GAN has higher accuracy in the classification of AD state and better performance in AD-related image processing (e.g. image denoising and segmentation). Most studies used data from public databases but lacked clinical validation, and the process of quantitative assessment and comparison in these studies lacked clinicians' participation, which may have an impact on the improvement of generation effect and generalization ability of the GAN model. The application value of GANs in the classification of AD state and AD-related image processing has been confirmed in reviewed studies. Improvement methods toward better GAN architecture were also discussed in this paper. In sum, the present study demonstrated advancing diagnostic performance and clinical applicability of GAN for AD, and suggested that the future researchers should consider recruiting clinicians to compare the algorithm with clinician manual methods and evaluate the clinical effect of the algorithm.