Key result
Adding hemodynamic and shape features to PHASES score improves aneurysm rupture classification to ~0.71 AUC.
Why the study?
Accurately quantifying rupture risk in unruptured intracranial aneurysms remains an unmet clinical challenge, and it was unclear whether computational fluid dynamics and morphological measurements add predictive value beyond the established PHASES score.
Does incorporating image-derived computational fluid dynamics and shape analysis features into the PHASES score improve the classification of aneurysm rupture status in patients with intracranial aneurysms?
Cross-Sectional (n=170)
Yes
Does incorporating image-derived computational fluid dynamics and shape analysis features into the PHASES score improve the classification of aneurysm rupture status in patients with intracranial aneurysms?
Integrating image-based computational fluid dynamics and shape analysis with the clinical PHASES score improves the classification accuracy of intracranial aneurysm rupture status from an AUC of 0.63 to 0.71.
Answers are grounded in this paper and cite their sources.
May refine PHASES-based rupture classification; leaves open prospective validation before clinical use.
PURPOSE: Accurately quantifying the rupture risk of unruptured intracranial aneurysms (UIAs) is crucial for guiding treatment decisions and remains an unmet clinical challenge. Computational Flow Dynamics and morphological measurements have been shown to differ between ruptured and unruptured aneurysms. It is not clear if these provide any additional information above routinely available clinical observations or not. Therefore, this study investigates whether incorporating image-derived features into the established PHASES score can improve the classification of aneurysm rupture status. METHODS: A cross-sectional dataset of 170 patients (78 with ruptured aneurysm) was used. Computational fluid dynamics (CFD) and shape analysis were performed on patients' images to extract additional features. These derived features were combined with PHASES variables to develop five ridge constrained logistic regression models for classifying the aneurysm rupture status. Correlation analysis and principal component analysis were employed for image-derived feature reduction. The dataset was split into training and validation subsets, and a ten-fold cross validation strategy with grid search optimisation and bootstrap resampling was adopted for determining the models' coefficients. Models' performances were evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: The logistic regression model based solely on PHASES achieved AUC of 0.63. All models incorporating derived features from CFD and shape analysis demonstrated improved performance, reaching an AUC of 0.71. Non-sphericity index (shape variable) and maximum oscillatory shear index (CFD variable) were the strongest predictors of a ruptured status. CONCLUSION: This study demonstrates the benefits of integrating image-based fluid dynamics and shape analysis with clinical data for improving the classification accuracy of aneurysm rupture status. Further evaluation using longitudinal data is needed to assess the potential for clinical integration.
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Benemerito et al. (2024) conducted a cross-sectional in Intracranial aneurysm (n=170). PHASES clinical variables combined with computational fluid dynamics (CFD) and shape analysis features vs. PHASES clinical variables alone was evaluated on Classification of intracranial aneurysm rupture status (Area Under the ROC Curve [AUC]) (AUC 0.71 vs 0.64). Incorporating computational fluid dynamics and morphological shape features into PHASES clinical variables improved intracranial aneurysm rupture status classification from an AUC of 0.64 to 0.71.
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