Novel Models for Identification of the Ruptured Aneurysm in Patients with Subarachnoid Hemorrhage with Multiple Aneurysms

Prediction of small intracranial aneurysm rupture status based on combined Clinical-Radiomics model

Background

Accumulating small unruptured intracranial aneurysms are detected due to the improved quality and higher frequency of cranial imaging, but treatment remains controversial. While surgery or endovascular treatment is effective for small aneurysms with a high risk of rupture, such interventions are unnecessary for aneurysms with a low risk of rupture. Consequently, it is imperative to accurately identify small aneurysms with a low risk of rupture. The purpose of this study was to develop a clinically practical model to predict small aneurysm ruptures based on a radiomics signature and clinical risk factors.

Methods

A total of 293 patients having an aneurysm with a diameter of less than 5 mm, including 199 patients (67.9 %) with a ruptured aneurysm and 94 patients (32.1 %) without a ruptured aneurysm, were included in this study. Digital subtraction angiography or surgical treatment was required in all cases. Data on the clinical risk factors and the features on computed tomography angiography images associated with the aneurysm rupture status were collected simultaneously. We developed a clinical-radiomics model to predict aneurysm rupture status using multivariate logistic regression analysis. The combined clinical-radiomics model was constructed by nomogram analysis. The diagnostic performance, clinical utility, and model calibration were evaluated by operating characteristic curve analysis, decision curve analysis, and calibration analysis.

Results

A combined clinical-radiomics model (Area Under Curve [AUC], 0.85; 95 % confidence interval [CI], 0.757-0.947) showed effective performance in the operating characteristic curve analysis. In the validation cohort, the performance of the combined model was better than that of the radiomics model (AUC, 0.75; 95 % CI, 0.645-0.865; Delong's test p-value = 0.01) and the clinical model (AUC, 0.74; 95 % CI, 0.625-0.851; Delong's test p-value <0.01) alone. The results of the decision curve, nomogram, and calibration analyses demonstrated the clinical utility and good fitness of the combined model.

Conclusion

Our study demonstrated the effectiveness of a clinical-radiomics model for predicting rupture status in small aneurysms.

Evaluation of unruptured aneurysm scoring systems and ratios in subarachnoid hemorrhage patients with multiple intracranial aneurysms

Background

This study aims to appraise aneurysm scores and ratios' ability to discriminate between ruptured aneurysms and unruptured intracranial aneurysms (UIAs) in subarachnoid hemorrhage (SAH) patients harboring multiple intracranial aneurysms (MICAs). We, then, investigate the most frequent risk factors associated with MICAs.

Methods

We retrospectively applied unruptured intracranial aneurysm treatment score (UIATS) and population hypertension age size of aneurysm earlier SAH from another aneurysm site of aneurysm (PHASES) score, aspect, and dome-to-neck ratio to the 59 consecutive spontaneous SAH patients with MICAs admitted between January 2000 and December 2015 to the Department of Neurosurgery of the University Hospital Center "Hôpital des Spécialités" of Rabat (Morocco). Patients with at least two intracranial aneurysms (IAs) confirmed on angiography were included in the study.

Results

Fifty-nine patients were harboring 128 IAs. The most frequent patient-level risk factors were arterial hypertension (AHT) 30.5 % (n = 18) and smoking status 22.0 % (n = 13). A PHASES score recommended treatment in 52 of 60 ruptured aneurysms and in six of 68 UIAs with a sensitivity of 31.67% and a specificity of 76.47%. UIATS recommended treatment in 26 of 62 ruptured aneurysms and in 35 of 55 UIAs with a sensitivity of 41.9% and a specificity of 63.6%. Aspect ratio recommended treatment in 60 of 60 ruptured aneurysms and in 63 of 68 UIAs with a sensitivity of 100% and a specificity of 88.24%. Dome-to-neck ratio recommended treatment in 45 of 60 ruptured aneurysms and in 48 of 68 UIAs with a sensitivity of 80% and a specificity of 63.24%. The aspect ratio (area under the curve [AUC] = 0.953) AUC > 0.8 has a higher discriminatory power between ruptured aneurysms and UIAs.

Conclusion

AHT and smoking status were the most common risk factors for intracranial multiple aneurysms and the aspect ratio and PHASES score were the most powerful discrimination tools between ruptured aneurysms and the UIAs.

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.

Semiautomated 3D mapping of aneurysmal wall enhancement with 7T-MRI

Aneurysm wall enhancement (AWE) after the administration of contrast gadolinium is a potential biomarker of unstable intracranial aneurysms. While most studies determine AWE subjectively, this study comprehensively quantified AWE in 3D imaging using a semi-automated method. Thirty patients with 33 unruptured intracranial aneurysms prospectively underwent high-resolution imaging with 7T-MRI. The signal intensity (SI) of the aneurysm wall was mapped and normalized to the pituitary stalk (PS) and corpus callosum (CC). The CC proved to be a more reliable normalizing structure in detecting contrast enhancement (p < 0.0001). 3D-heatmaps and histogram analysis of AWE were used to generate the following metrics: specific aneurysm wall enhancement (SAWE), general aneurysm wall enhancement (GAWE) and focal aneurysm wall enhancement (FAWE). GAWE was more accurate in detecting known morphological determinants of aneurysm instability such as size ≥ 7 mm (p = 0.049), size ratio (p = 0.01) and aspect ratio (p = 0.002). SAWE and FAWE were aneurysm specific metrics used to characterize enhancement patterns within the aneurysm wall and the distribution of enhancement along the aneurysm. Blebs were easily identified on 3D-heatmaps and were more enhancing than aneurysm sacs (p = 0.0017). 3D-AWE mapping may be a powerful objective tool in characterizing different biological processes of the aneurysm wall.

Morphology-based radiomics signature: a novel determinant to identify multiple intracranial aneurysms rupture

We aimed to develop and validate a morphology-based radiomics signature nomogram for assessing the risk of intracranial aneurysm (IA) rupture. A total of 254 aneurysms in 105 patients with subarachnoid hemorrhage and multiple intracranial aneurysms from three centers were retrospectively reviewed and randomly divided into the derivation and validation cohorts. Radiomics morphological features were automatically extracted from digital subtraction angiography and selected by the least absolute shrinkage and selection operator algorithm to develop a radiomics signature. A radiomics signature-based nomogram was developed by incorporating the signature and traditional morphological features. The performance of calibration, discrimination, and clinical usefulness of the nomogram was assessed. Ten radiomics morphological features were selected to build the radiomics signature model, which showed better discrimination with an area under the curve (AUC) equal to 0.814 and 0.835 in the derivation and validation cohorts compared with 0.747 and 0.666 in the traditional model, which only include traditional morphological features. When radiomics signature and traditional morphological features were combined, the AUC increased to 0.842 and 0.849 in the derivation and validation cohorts, thus showing better performance in assessing aneurysm rupture risk. This novel model could be useful for decision-making and risk stratification for patients with IAs.

Development and validation of a novel nomogram to predict aneurysm rupture in patients with multiple intracranial aneurysms: a multicentre retrospective study

Background and purpose

Approximately 15%–45% of patients with unruptured intracranial aneurysms have multiple intracranial aneurysms (MIAs). Determining which one is most likely to rupture is extremely important for treatment decision making for MIAs patients. This study aimed to develop and validate a nomogram to evaluate the per-aneurysm rupture risk of MIAs patients.

Methods

A total of 1671 IAs from 700 patients with MIAs were randomly dichotomised into derivation and validation sets. Multivariate logistic regression analysis was used to select predictors and construct a nomogram model for aneurysm rupture risk assessment in the derivation set. The discriminative accuracy, calibration performance and clinical usefulness of this nomogram were assessed. We also developed a multivariate model for a subgroup of 158 subarachnoid haemorrhage (SAH) patients and compared its performance with the nomogram model.

Results

Multivariate analyses identified seven variables that were significantly associated with IA rupture (history of SAH, alcohol consumption, female sex, aspect ratio >1.5, posterior circulation, irregular shape and bifurcation location). The clinical and morphological-based MIAs (CMB-MIAs) nomogram model showed good calibration and discrimination (derivation set: area under the curve (AUC)=0.740 validation set: AUC=0.772). Decision curve analysis demonstrated that the nomogram was clinically useful. Compared with the nomogram model, the AUC of multivariate model developed from SAH patients had lower value of 0.730.

Conclusions

This CMB-MIAs nomogram for MIAs rupture risk is the first to be developed and validated in a large multi-institutional cohort. This nomogram could be used in decision-making and risk stratification in MIAs patients.

Risk factors and treatment approach for subarachnoid hemorrhage in a patient with nine intracranial aneurysms

Introduction. In about one-third of the patients with aneurysmal subarachnoid bleeding, multiple intracranial aneurysms are confirmed. Risk factors such as female gender, smoking, hypertension, and age over 60 tend to be associated with multiple aneurysms. In this paper, we also discuss family predisposition and the treatment approach for multiple cerebral aneurysms. Case outline. Here, we present a case of a female patient, 64-year-old, with spontaneous subarachnoid hemorrhage that had nine intracranial aneurysms. The patient was treated for hypertension for a longer period, excessive smoker, and two of her nearest members of the family died from intracranial bleeding. The patient was fully conscious, without any neurological impairment. Subarachnoid bleeding was diffuse and nor brain-computer tomography finding nor digital subtraction angiography couldn't suggest the source or location of bleeding among nine presented aneurisms. Magnet resonance imaging had to be done, and the T1W fast spin-echo sequence showed a 9 mm large ruptured an aneurysm at the basilar tip, after contrast application, beside others. Three days after insult endovascular embolization was done and two basilar aneurysms were excluded from the circulation, including the one that bled. Conclusion. The patient had the majority of risk factors for multiple intracranial aneurysms. Knowledge of the family predisposition of multiple intracranial aneurysms allowed us to make proper diagnostics of a patient's descendant and reveal a new patient.