Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor

Integrating Clinical Data and Radiomics and Deep Learning Features for End-to-End Delayed Cerebral Ischemia Prediction on Noncontrast CT

BACKGROUND AND PURPOSE

Delayed cerebral ischemia is hard to diagnose early due to gradual, symptomless development. This study aimed to develop an automated model for predicting delayed cerebral ischemia following aneurysmal SAH on NCCT.

MATERIALS AND METHODS

This retrospective study included 400 patients with aneurysmal SAH (156 with delayed cerebral ischemia) who underwent NCCT. The study used ATT-Deeplabv3+ for automatically segmenting hemorrhagic regions using semisupervised learning. Principal component analysis was used for reducing the dimensionality of deep learning features extracted from the average pooling layer of ATT-DeepLabv3+. The classification model integrated clinical data, radiomics, and deep learning features to predict delayed cerebral ischemia. Feature selection involved Pearson correlation coefficients, least absolute shrinkage, and selection operator regression. We developed models based on clinical features, clinical-radiomics, and a combination of clinical, radiomics, and deep learning. The study selected logistic regression, Naive Bayes, Adaptive Boosting (AdaBoost), and multilayer perceptron as classifiers. The performance of segmentation and classification models was evaluated on their testing sets using the Dice similarity coefficient for segmentation, and the area under the receiver operating characteristic curve (AUC) and calibration curves for classification.

RESULTS

The segmentation process achieved a Dice similarity coefficient of 0.91 and the average time of 0.037 s/image. Seventeen features were selected to calculate the radiomics score. The clinical-radiomics-deep learning model with multilayer perceptron achieved the highest AUC of 0.84 (95% CI, 0.72-0.97), which outperformed the clinical-radiomics model (P = .002) and the clinical features model (P = .001) with multilayer perceptron. The performance of clinical-radiomics-deep learning model using AdaBoost was significantly superior to its clinical-radiomics model (P = .027). The performance of the clinical-radiomics-deep learning model and the clinical-radiomics model with logistic regression notably exceeded that of the model based solely on clinical features (P = .028; P = .046). The AUC of the clinical-radiomics-deep learning model with multilayer perceptron (P < .001) and the clinical-radiomics model with logistic regression (P = .046) were significantly higher than the clinical model with logistic regression. Of all models, the clinical-radiomics-deep learning model with multilayer perceptron showed best calibration.

CONCLUSIONS

The proposed 2-stage end-to-end model not only achieves rapid and accurate segmentation but also demonstrates superior diagnostic performance with high AUC values and good calibration in the clinical-radiomics-deep learning model, suggesting its potential to enhance delayed cerebral ischemia detection and treatment strategies.

Plasma proteomics analysis reveals potential biomarkers for intracranial aneurysm formation and rupture

The aim of this study was to investigate the plasma proteome in individuals with intracranial aneurysms (IAs) and identify biomarkers associated with the formation and rupture of IAs. Proteomic profiles (N = 1069 proteins) were assayed in plasma (N = 120) collected from patients with ruptured and unruptured intracranial aneurysms (RIA and UIA), traumatic subarachnoid hemorrhage (tSAH), and healthy controls (HC) using tandem mass tag (TMT) labeling quantitative proteomics analysis. Gene ontology (GO) and pathway analysis revealed that these relevant proteins were involved in immune response and extracellular matrix organization pathways. Seven candidate biomarkers were verified by ELISA in a completely separate cohort for validation (N = 90). Among them, FN1, PON1, and SERPINA1 can be utilized as diagnosis biomarkers of IA, with a combined area under the ROC curve of 0.891. The sensitivity was 93.33%, specificity was 75.86%, and accuracy was 87.64%. PFN1, ApoA-1, and SERPINA1 can serve as independent risk factors for predicting aneurysm rupture. The combined prediction of aneurysm rupture yielded an area under the ROC curve of 0.954 with a sensitivity of 96.15%, specificity of 81.48%, and accuracy of 88.68%. This prediction model was more effective than PHASES score. In conclusion, high-throughput proteomics analysis with population validation was performed to assess blood-based protein expression characteristics. This revealed the potential mechanism of IA formation and rupture, facilitating the discovery of biomarkers. SIGNIFICANCE: Although the annual rupture rate of small unruptured aneurysms is believed to be minimal, studies have indicated that ruptured aneurysms typically have an average size of 6.28 mm, with 71.8% of them being <7 mm in diameter. Hence, evaluating the possibility of rupture in UIA and making a choice between aggressive treatment and conservative observation emerges as a significant challenge in the management of UIA. No biomarker or scoring system has been able to satisfactorily address this issue to date. It would be significant to develop biomarkers that could be used for early diagnosis of IA as well as for prediction of IA rupture. After TMT proteomics analysis and ELISA validation in independent populations, we found that FN1, PON1, and SERPINA1 can be utilized as diagnostic biomarkers for IA, and PFN1, ApoA-1, and SERPINA1 can serve as independent risk factors for predicting aneurysm rupture. Especially, when combined with ApoA-1, SERPINA1, and PFN1 for predicting IA rupture, the area under the curve (AUC) was 0.954 with a sensitivity of 96.15%, specificity of 81.48%, and accuracy of 88.68%. This prediction model was more effective than PHASES score.

Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs

BACKGROUND

The rupture of intracranial aneurysms (IAs) would result in subarachnoid hemorrhage with high mortality and disability. Predicting the risk of IAs rupture remains a challenge.

METHODS

This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model and machine learning algorithms. First, medical image segmentation and reconstruction algorithms were applied to 3D Digital Subtraction Angiography (DSA) imaging data to construct three-dimensional IAs geometric models. Geometrical parameters of IAs were then acquired using Geomagic, followed by the computation of hemodynamic clouds and hemodynamic parameters using Computational Fluid Dynamics (CFD). A PointNet-based model was developed to extract different dimensional hemodynamic cloud features. Finally, five types of machine learning algorithms were applied on geometrical parameters, hemodynamic parameters, and hemodynamic cloud features to classify and recognize IAs rupture status. The classification performance of different dimensional hemodynamic cloud features was also compared.

RESULTS

The 16-, 32-, 64-, and 1024-dimensional hemodynamic cloud features were extracted with the PointNet-based model, respectively, and the four types of cloud features in combination with the geometrical parameters and hemodynamic parameters were respectively applied to classify the rupture status of IAs. The best classification outcomes were achieved in the case of 16-dimensional hemodynamic cloud features, the accuracy of XGBoost, CatBoost, SVM, LightGBM, and LR algorithms was 0.887, 0.857, 0.854, 0.857, and 0.908, respectively, and the AUCs were 0.917, 0.934, 0.946, 0.920, and 0.944. In contrast, when only utilizing geometrical parameters and hemodynamic parameters, the accuracies were 0.836, 0.816, 0.826, 0.832, and 0.885, respectively, with AUC values of 0.908, 0.922, 0.930, 0.884, and 0.921.

CONCLUSION

In this paper, classification models for IAs rupture status were constructed by integrating a PointNet-based model and machine learning algorithms. Experiments demonstrated that hemodynamic cloud features had a certain contribution weight to the classification of IAs rupture status. When 16-dimensional hemodynamic cloud features were added to the morphological and hemodynamic features, the models achieved the highest classification accuracies and AUCs. Our models and algorithms would provide valuable insights for the clinical diagnosis and treatment of IAs.

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

A dynamic nomogram for predicting intraoperative brain bulge during decompressive craniectomy in patients with traumatic brain injury: a retrospective study

OBJECTIVE

The aim of this paper is to investigate the risk factors associated with intraoperative brain bulge (IOBB), especially the computed tomography (CT) value of the diseased lateral transverse sinus, and to develop a reliable predictive model to alert neurosurgeons to the possibility of IOBB.

METHODS

A retrospective analysis was performed on 937 patients undergoing traumatic decompressive craniectomy. A total of 644 patients from Fuzong Clinical Medical College of Fujian Medical University were included in the development cohort, and 293 patients from the First Affiliated Hospital of Shantou University Medical College were included in the external validation cohort. Univariate and multifactorial logistic regression analyses identified independent risk factors associated with IOBB. The logistic regression models consisted of independent risk factors, and receiver operating characteristic curves, calibration, and decision curve analyses were used to assess the performance of the models. Various machine learning models were used to compare with the logistic regression model and analyze the importance of the factors, which were eventually jointly developed into a dynamic nomogram for predicting IOBB and published online in the form of a simple calculator.

RESULTS

IOBB occurred in 93/644 (14.4%) patients in the developmental cohort and 47/293 (16.0%) in the validation cohort. Univariate and multifactorial regression analyses showed that age, subdural hematoma, contralateral fracture, brain contusion, and CT value of the diseased lateral transverse sinus were associated with IOBB. A logistic regression model (full model) consisting of the above risk factors had excellent predictive power in both the development cohort [area under the curve (AUC)=0.930] and the validation cohort (AUC=0.913). Among the four machine learning models, the AdaBoost model showed the best predictive value (AUC=0.998). Factors in the AdaBoost model were ranked by importance and combined with the full model to create a dynamic nomogram for clinical application, which was published online as a practical and easy-to-use calculator.

CONCLUSIONS

The CT value of the diseased lateral transverse is an independent risk factor and a reliable predictor of IOBB. The online dynamic nomogram formed by combining logistic regression analysis models and machine learning models can more accurately predict the possibility of IOBBs in patients undergoing traumatic decompressive craniectomy.

Risk factors of unruptured intracranial aneurysms instability in the elderly

BACKGROUND

Presently, a consistent strategy for determining the stability of unruptured intracranial aneurysms (UIAs) in elderly patients is lacking, primarily due to the unique characteristics of this demographic. Our objective was to assess the risk factors contributing to aneurysm instability (growth or rupture) within the elderly population.

METHODS

In this study, we compiled data from follow-up patients with UIAs spanning from November 2016 to August 2021. We specifically focused on patients aged ≥ 60 years. Clinical histories were gathered, and morphological parameters of aneurysms were measured. The growth of aneurysms was determined using the computer-assisted semi-automated measurement (CASAM). Growth and rupture rates of UIAs were calculated, and both univariate and multivariate Cox regression analyses were conducted. Additionally, Kaplan-Meier survival curves were plotted.

RESULTS

A total of 184 patients with 210 aneurysms were enrolled in the study. The follow-up period encompasses 506.6 aneurysm-years and 401.4 patient-years. Among all the aneurysms, 23 aneurysms exhibited growth, with an annual aneurysm growth rate of 11.0%, and 1 (4.5%) experienced rupture, resulting in an annual aneurysm rupture rate of 0.21%. Multivariate Cox analysis identified poorly controlled hypertension (P = 0.011) and high-risk aneurysms (including anterior cerebral artery (ACA), anterior communicating artery (AcoA), posterior communicating artery aneurysm (PcoA), posterior circulation (PC) > 4 mm or distal internal carotid artery (ICAd), middle cerebral artery (MCA), and PC > 7 mm) (P = 0.006) as independent risk factors for the development of unstable aneurysms.

CONCLUSIONS

In the elderly, poorly controlled hypertension and high-risk aneurysms emerge as significant risk factors for aneurysm instability. This underscores the importance of rigorous surveillance or timely intervention in patients presenting with these risk factors.

Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants

It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intracranial aneurysms (UIAs) using a temporal train-test split. Temporal train-test splits allow to simulate prospective validation, and therefore provide more accurate estimations of a model's predictive quality when applied to future patients. ML models for the prediction of the Glasgow outcome scale, modified Rankin Scale (mRS), and new transient or permanent neurological deficits (output variables) were created from all UIA patients that underwent microsurgery at the Kepler University Hospital Linz (Austria) between 2002 and 2020 (n = 466), based on 18 patient- and 10 aneurysm-specific preoperative parameters (input variables). Train-test splitting was performed with a temporal split for outcome prediction in microsurgical therapy of UIA. Moreover, an external validation was conducted on an independent external data set (n = 256) of the Department of Neurosurgery, University Medical Centre Hamburg-Eppendorf. In total, 722 aneurysms were included in this study. A postoperative mRS > 2 was best predicted by a quadratic discriminant analysis (QDA) estimator in the internal test set, with an area under the receiver operating characteristic curve (ROC-AUC) of 0.87 ± 0.03 and a sensitivity and specificity of 0.83 ± 0.08 and 0.71 ± 0.07, respectively. A Multilayer Perceptron predicted the post- to preoperative mRS difference > 1 with a ROC-AUC of 0.70 ± 0.02 and a sensitivity and specificity of 0.74 ± 0.07 and 0.50 ± 0.04, respectively. The QDA was the best model for predicting a permanent new neurological deficit with a ROC-AUC of 0.71 ± 0.04 and a sensitivity and specificity of 0.65 ± 0.24 and 0.60 ± 0.12, respectively. Furthermore, these models performed significantly better than the classic logistic regression models (p < 0.0001). The present results showed good performance in predicting functional and clinical outcomes after microsurgical therapy of UIAs in the internal data set, especially for the main outcome parameters, mRS and permanent neurological deficit. The external validation showed poor discrimination with ROC-AUC values of 0.61, 0.53 and 0.58 respectively for predicting a postoperative mRS > 2, a pre- and postoperative difference in mRS > 1 point and a GOS < 5. Therefore, generalizability of the models could not be demonstrated in the external validation. A SHapley Additive exPlanations (SHAP) analysis revealed that this is due to the most important features being distributed quite differently in the internal and external data sets. The implementation of newly available data and the merging of larger databases to form more broad-based predictive models is imperative in the future.

Navigating complexity: a comprehensive review of microcatheter shaping techniques in endovascular aneurysm embolization

The endovascular intervention technique has gained prominence in the treatment of intracranial aneurysms due to its minimal invasiveness and shorter recovery time. A critical step of the intervention is the shaping of the microcatheter, which ensures its accurate placement and stability within the aneurysm sac. This is vital for enhancing coil placement and minimizing the risk of catheter kickback during the coiling process. Currently, microcatheter shaping is primarily reliant on the operator's experience, who shapes them based on the curvature of the target vessel and aneurysm location, utilizing 3D rotational angiography or CT angiography. Some researchers have documented their experiences with conventional shaping methods. Additionally, some scholars have explored auxiliary techniques such as 3D printing and computer simulations to facilitate microcatheter shaping. However, the shaping of microcatheters can still pose challenges, especially in cases with complex anatomical structures or very small aneurysms, and even experienced operators may encounter difficulties, and there has been a lack of a holistic summary of microcatheter shaping techniques in the literature. In this article, we present a review of the literature from 1994 to 2023 on microcatheter shaping techniques in endovascular aneurysm embolization. Our review aims to present a thorough overview of the various experiences and techniques shared by researchers over the last 3 decades, provides an analysis of shaping methods, and serves as an invaluable resource for both novice and experienced practitioners, highlighting the significance of understanding and mastering this technique for successful endovascular intervention in intracranial aneurysms.

Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis

Background

Intracranial aneurysm (IA) is a nodular protrusion of the arterial wall caused by the localized abnormal enlargement of the lumen of a brain artery, which is the primary cause of subarachnoid hemorrhage. Accurate rupture risk prediction can effectively aid treatment planning, but conventional rupture risk estimation based on clinical information is subjective and time-consuming.

Methods

We propose a novel classification method based on the CTA images for differentiating aneurysms that are prone to rupture. The main contribution of this study is that the learning-based method proposed in this study leverages deep learning and radiomics features and integrates clinical information for a more accurate prediction of the risk of rupture. Specifically, we first extracted the provided aneurysm regions from the CTA images as 3D patches with the lesions located at their centers. Then, we employed an encoder using a 3D convolutional neural network (CNN) to extract complex latent features automatically. These features were then combined with radiomics features and clinical information. We further applied the LASSO regression method to find optimal features that are highly relevant to the rupture risk information, which is fed into a support vector machine (SVM) for final rupture risk prediction.

Results

The experimental results demonstrate that our classification method can achieve accuracy and AUC scores of 89.78% and 89.09%, respectively, outperforming all the alternative methods.

Discussion

Our study indicates that the incorporation of CNN and radiomics analysis can improve the prediction performance, and the selected optimal feature set can provide essential biomarkers for the determination of rupture risk, which is also of great clinical importance for individualized treatment planning and patient care of IA.