Association Between Serum Homocysteine Concentration, Aneurysm Wall Inflammation, and Aneurysm Symptoms in Intracranial Fusiform Aneurysm.

RATIONALE AND OBJECTIVES: The pathophysiology of fusiform intracranial aneurysm (FIA) involves inflammatory processes, and homocysteine plays a role in the inflammatory processes in the vessel wall. Moreover, aneurysm wall enhancement (AWE) has emerged as a new imaging biomarker of aneurysm wall inflammatory pathologies. To investigate the pathophysiological mechanisms of aneurysm wall inflammation and FIA instability, we aimed to determine the associations between the homocysteine concentration, AWE, and FIAs' related symptoms. MATERIALS AND METHODS: We retrospectively reviewed the data of 53 patients with FIA who underwent both high-resolution magnetic resonance imaging and serum homocysteine concentration measurement. FIAs' related symptoms were defined as ischemic stroke or transient ischemic attack, cranial nerve compression, brainstem compression, and acute headache. The contrast ratio of the signal intensity of the aneurysm wall to the pituitary stalk (CRstalk) was used to indicate AWE. Multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were performed to determine how well the independent factors could predict FIAs' related symptoms. Predictors of CRstalk were also investigated. Spearman's correlation coefficient was used to identify the potential associations between these predictors. RESULTS: Fifty-three patients were included, of whom 23 (43.4%) presented with FIAs' related symptoms. After adjusting for baseline differences in the multivariate logistic regression analysis, the CRstalk (odds ratio [OR]=3.207, P = .023) and homocysteine concentration (OR=1.344, P = .015) independently predicted FIAs' related symptoms. The CRstalk was able to differentiate between FIAs with and without symptoms (area under the ROC curve [AUC]=0.805), with an optimal cutoff value of 0.76. The homocysteine concentration could also differentiate between FIAs with and without symptoms (AUC=0.788), with an optimal cutoff value of 13.13. The combination of the CRstalk and homocysteine concentration had a better ability to identify symptomatic FIAs (AUC=0.857). Male sex (OR=0.536, P = .018), FIAs' related symptoms (OR=1.292, P = .038), and homocysteine concentration (OR=1.254, P = .045) independently predicted the CRstalk. CONCLUSION: A higher serum homocysteine concentration and greater AWE indicate FIA instability. Serum homocysteine concentration may be a useful biomarker of FIA instability; however, this needs to be verified in future studies.

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.

OBJECTIVES: To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework. METHODS: A retrospective diagnostic study was conducted based on 159 IAs from 136 patients who underwent the T1 images. Among them, 127 cases were randomly selected for training and validation, and 32 cases were used to assess the accuracy and consistency of our algorithm. We developed and assembled three convolutional neural networks for the segmentation and detection of IAs. The segmentation and detection performance of the model were compared with the ground truth, and various metrics were calculated at the voxel level, IAs level, and patient level to show the performance of our framework. RESULTS: Our assembled model achieved overall Dice, voxel-level sensitivity, specificity, balanced accuracy, and F1 score of 0.802, 0.874, 0.9998, 0.937, and 0.802, respectively. A coincidence greater than 0.7 between the aneurysms predicted by the model and the ground truth was considered as a true positive. For IAs detection, the sensitivity reached 90.63% with 0.58 false positives per case. The volume of IAs segmented by our model showed a high agreement and consistency with the volume of IAs labeled by experts. CONCLUSION: The deep learning framework is achievable and robust for IAs segmentation and detection. Our model offers more clinical application opportunities compared to digital subtraction angiography (DSA)-based, CTA-based, and MRA-based methods. CLINICAL RELEVANCE STATEMENT: Our deep learning framework effectively detects and segments intracranial aneurysms using clinical routine T1 sequences, showing remarkable effectiveness and offering great potential for improving the detection of latent intracranial aneurysms and enabling early identification. KEY POINTS: •There is no segmentation method based on clinical routine T1 images. Our study shows that the proper deep learning framework can effectively localize the intracranial aneurysms. •The T1-based segmentation and detection method is more universal than other angiography-based detection methods, which can potentially reduce missed diagnoses caused by the absence of angiography images. •The deep learning framework is robust and has the potential to be applied in a clinical setting.

Statins may Decrease Aneurysm wall Enhancement of Unruptured Fusiform Intracranial Aneurysms: A high-resolution 3T MRI Study.

Inflammation plays an integral role in the formation, growth, and progression to rupture of unruptured intracranial aneurysms. Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) has emerged as a surrogate biomarker of vessel wall inflammation and unruptured intracranial aneurysm instability. We investigated the correlation between anti-inflammatory drug use and three-dimensional AWE of fusiform intracranial aneurysms (FIAs). We retrospectively analyzed consecutive patients with FIAs in our database who underwent 3T HR-MRI at three Chinese centers. FIAs were classified as fusiform-type, dolichoectatic-type, or transitional-type. AWE was objectively defined using the aneurysm-to-pituitary stalk contrast ratio in three-dimensional space by determining the contrast ratio of the average signal intensity in the aneurysmal wall and pituitary stalk on post-contrast T1-weighted images. Data on aneurysm size, morphology, and location, as well as patient demographics and comorbidities, were collected. Univariate and multivariate logistic regression analyses were performed to determine factors independently associated with AWE of FIAs on HR-MRI. In total, 127 FIAs were included. In multivariate analysis, statin use (ß = -0.236, P = 0.007) was the only independent factor significantly associated with decreased AWE. In the analysis of three FIA subtypes, the fusiform and transitional types were significantly associated with statin use (rs = -0.230, P = 0.035; and rs = -0.551, P = 0.010; respectively). It establishes an incidental correlation between the use of statins daily for ≥ 6 months and decreased AWE of FIAs. The findings also indicate that the pathophysiology may differ among the three FIA subtypes.

Three-dimensional aneurysm wall enhancement in fusiform intracranial aneurysms is associated with aneurysmal symptoms.

Background and purpose: Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) is a potential biomarker for evaluating unstable aneurysms. Fusiform intracranial aneurysms (FIAs) frequently have a complex and curved structure. We aimed to develop a new three-dimensional (3D) aneurysmal wall enhancement (AWE) characterization method to enable comprehensive FIA evaluation and to investigate the ability of 3D-AWE to predict symptomatic FIA. Methods: We prospectively recruited patients with unruptured FIAs and received 3 T HR-MRI imaging from September 2017 to January 2019. 3D models of aneurysms and parent arteries were generated. Boundaries of the FIA were determined using 3D vessel diameter measurements. Dmax was the greatest diameter in the cross-section, while Lmax was the length of the centerline of the aneurysm. Signal intensity of the FIA was normalized to the pituitary stalk and then mapped onto the 3D model, then the average enhancement (3D-AWEavg), maximum enhancement (3D-AWEmax), enhancement area (AWEarea), and enhancement ratio (AWEratio) were calculated as AWE indicators, and the surface area of the entire aneurysm (Aarea) was also calculated. Areas with high AWE were defined as those with a value >0.9 times the signal intensity of the pituitary stalk. Multivariable logistic regression analyses were performed to determine independent predictors of aneurysm-related symptoms. FIA subtypes were defined as fusiform, dolichoectasia, and transitional. Differences between the three FIA subtypes were also examined. Results: Forty-seven patients with 47 FIAs were included. Mean patient age was 55 ± 12.62 years and 74.5% were male. Twenty-nine patients (38.3%) were symptomatic. After adjusting for baseline differences in age, hypertension, Lmax, and FIA subtype, the multivariate logistics regression models showed that 3D-AWEavg (odds ratio [OR], 4.029; p = 0.019), 3D-AWEmax (OR, 3.437; p = 0.022), AWEarea (OR, 1.019; p = 0.008), and AWEratio (OR, 2.490; p = 0.045) were independent predictors of aneurysm-related symptoms. Dmax and Aarea were larger and 3D-AWEavg, 3D-AWEmax, AWEarea, and AWEratio were higher with the transitional subtype than the other two subtypes. Conclusion: The new 3D AWE method, which enables the use of numerous new metrics, can predict symptomatic FIAs. Different 3D-AWE between the three FIA subtypes may be helpful in understanding the pathophysiology of FIAs.

Systemic immune-inflammation index is associated with aneurysmal wall enhancement in unruptured intracranial fusiform aneurysms.

Introduction: Inflammation plays a key role in the progression of intracranial aneurysms. Aneurysmal wall enhancement (AWE) correlates well with inflammatory processes in the aneurysmal wall. Understanding the potential associations between blood inflammatory indices and AWE may aid in the further understanding of intracranial aneurysm pathophysiology. Methods: We retrospectively reviewed 122 patients with intracranial fusiform aneurysms (IFAs) who underwent both high-resolution magnetic resonance imaging and blood laboratory tests. AWE was defined as a contrast ratio of the signal intensity of the aneurysmal wall to that of the pituitary stalk ≥ 0.90. The systemic immune-inflammation (SII) index (neutrophils × platelets/lymphocytes) was calculated from laboratory data and dichotomized based on whether or not the IFA had AWE. Aneurysmal symptoms were defined as sentinel headache or oculomotor nerve palsy. Multivariable logistic regression and receiver operating characteristic curve analyses were performed to determine how well the SII index was able to predict AWE and aneurysmal symptoms. Spearman's correlation coefficients were used to explore the potential associations between variables. Results: This study included 95 patients, of whom 24 (25.3%) presented with AWE. After adjusting for baseline differences in neutrophil to lymphocyte ratios, leukocytes, and neutrophils in the multivariable logistic regression analysis, smoking history (P = 0.002), aneurysmal symptoms (P = 0.047), maximum diameter (P = 0.048), and SII index (P = 0.022) all predicted AWE. The SII index (P = 0.038) was the only independent predictor of aneurysmal symptoms. The receiver operating characteristic curve analysis revealed that the SII index was able to accurately distinguish IFAs with AWE (area under the curve = 0.746) and aneurysmal symptoms (area under the curve = 0.739). Discussion: An early elevation in the SII index can independently predict AWE in IFAs and is a potential new biomarker for predicting IFA instability.

Aneurysm wall enhancement, atherosclerotic proteins, and aneurysm size may be related in unruptured intracranial fusiform aneurysms.

OBJECTIVE: This cross-sectional study aimed to investigate the associations between aneurysm wall enhancement (AWE), atherosclerotic protein levels, and aneurysm size in unruptured intracranial fusiform aneurysms (IFAs). METHODS: Patients with IFAs underwent high-resolution magnetic resonance imaging (HR-MRI) and atherosclerotic protein examinations from May 2015 to December 2021 were collected. A CRstalk (signal intensity [SI] of IFA wall/SI of pituitary stalk) > 0.60 was considered to indicate AWE. Atherosclerotic protein data was obtained from the peripheral blood. Aneurysmal characteristics included the maximal diameter of the cross-section (Dmax), location, type of IFA, presence of mural thrombus, and mural clots. Statistical analyses were performed with univariate analysis, logistic regression analysis, and Spearman's correlation coefficient. RESULTS: Seventy-one IFAs from 71 patients were included in the study. Multivariate analysis revealed statin use (OR = 0.189, p = 0.026) and apolipoprotein B (Apo-B) level (OR = 6.019, p = 0.026) were the independent predictors of AWE in IFAs. In addition, statin use (OR = 0.813, p = 0.036) and Apo-B level (OR = 1.610, p = 0.003) were also the independent predictors of CRstalk. Additionally, we found that CRstalk and AWE were significantly positively associated with Dmax (rs = 0.409 and 0.349, respectively; p < 0.001 and p = 0.003, respectively). CONCLUSIONS: There may be correlations between AWE, atherosclerotic protein levels, and aneurysm size in patients with IFAs. Apo-B and statin use were independent predictors of AWE in IFAs, which have the potential to be new therapeutic targets for IFAs. KEY POINTS: • There may be correlations between aneurysm wall enhancement, atherosclerotic protein levels in the peripheral blood, and aneurysm size in patients with intracranial fusiform aneurysms. • Apolipoprotein B and statin use were independent predictors of aneurysm wall enhancement in intracranial fusiform aneurysms.

Independent predictors and risk score for intraprocedural rupture during endovascular treatment of small ruptured intracranial aneurysms (<5 mm).

Background and purpose: Intraprocedural rupture (IPR) is a devastating complication of endovascular treatment (EVT). Small-sized and ruptured aneurysms are independent predictors of IPR, which presents a technical challenge during EVT. We aimed to develop a score to quantify the individual patient risk of IPR in the EVT of small (<5 mm) ruptured aneurysms (SRAs). Methods: A retrospective review was conducted to interrogate databases prospectively maintained at two academic institutions in China from January 2009 to October 2016. We collected intraoperative angiograms and medical records to identify independent predictors of IPR using univariate and multivariable analyses. A risk score for IPR was derived using multivariable logistic regression analyses. Results: Of the 290 enrolled patients, IPR occurred in 16 patients (5.5%). The univariate analysis showed that the rate of IPR was significantly higher in patients having aneurysms with a small basal outpouching (SBO), in patients having aneurysms concomitant with adjacent moderate atherosclerotic stenosis (ACAMAS), and in former or current smokers. Multivariate analyses showed that SBO [odds ratio (OR): 3.573; 95% confidence interval (CI): 1.078-11.840; p = 0.037], vascular eloquence (VE; OR: 3.780; 95% CI: 1.080-13.224; p = 0.037), and ACAMAS (OR: 6.086; 95% CI: 1.768-20.955; p = 0.004) were significantly and independently associated with IPR. A three-point risk score (S-V-A) was derived to predict IPR [SBO (yes = 1), VE (yes = 1), and ACAMAS (yes = 1)]. Conclusions: Intraprocedural rupture occurred in 5.5% of the patients during EVT of SRA. SBO, VE, and ACAMAS were independent risk factors of IPR in the EVT of SRA. Based on these variables, the S-V-A score may be useful in predicting IPR daily, but more confirmation studies are required.

Quantification of aneurysm wall enhancement in intracranial fusiform aneurysms and related predictors based on high-resolution magnetic resonance imaging: a validation study.

Background: Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) has emerged as a new imaging biomarker of intracranial aneurysm instability. Objective: To determine a standard method of AWE quantification for predicting fusiform intracranial aneurysms (FIAs) stability by comparing the sensitivity of each parameter in identifying symptomatic FIAs. The predictors of AWE and FIA types were also identified. Methods: We retrospectively analyzed consecutive fusiform aneurysm patients who underwent HR-MRI from two centers. The aneurysm-to-pituitary stalk contrast ratio (CRstalk), aneurysm enhancement ratio, and aneurysm enhancement index were extracted, and their sensitivities in discriminating aneurysm symptoms were compared using the receiver-operating characteristic curve. Morphological parameters of fusiform aneurysm were extracted based on 3D vessel model. Uni- and multivariate analyses of related predictors for AWE, CRstalk, and FIA types were performed, respectively. Results: Overall, 117 patients (mean age, 53.3 ± 11.7 years; male, 75.2%) with 117 FIAs underwent HR-MRI were included. CRstalk with the maximum signal intensity (CRstalk-max) had the highest sensitivity in identifying symptomatic FIAs with an area under the curve value (0.697) and a cut-off value of 0.90. The independent predictors of AWE were aneurysm symptoms [(odds ratio) OR = 3.754, p = 0.003], aspirin use (OR = 0.248, p = 0.037), and the maximum diameter of the cross-section (OR = 1.171, p = 0.043). The independent predictors of CRstalk-max were aneurysm symptoms (OR = 1.289, p = 0.003) and posterior circulation aneurysm (OR = 1.314, p = 0.001). Transitional-type showed higher rates of hypertension and mural thrombus over both dolichoectatic- and fusiform-type FIAs. Conclusion: CRstalk-max may be the most reliable parameter to quantify AWE to distinguish symptomatic FIAs. It also has the potential to identify unstable FIAs. Several factors contribute to the complex pathophysiology of FIAs and need further validation in a larger cohort.