Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture
Abstract Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Rupture, though rare, can cause devastating subarachnoid hemorrhage. This study analyzed 141 SIAs (101 unruptured, 40 ruptured) using semi-auto...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-08478-1 |
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| author | Vanessa M. Swiatek Samuel Voss Florian Sprenger Igor Fischer Hafez Kader Klaus-Peter Stein Roland Schwab Sylvia Saalfeld Ali Rashidi Daniel Behme Philipp Berg I. Erol Sandalcioglu Belal Neyazi |
| author_facet | Vanessa M. Swiatek Samuel Voss Florian Sprenger Igor Fischer Hafez Kader Klaus-Peter Stein Roland Schwab Sylvia Saalfeld Ali Rashidi Daniel Behme Philipp Berg I. Erol Sandalcioglu Belal Neyazi |
| author_sort | Vanessa M. Swiatek |
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| description | Abstract Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Rupture, though rare, can cause devastating subarachnoid hemorrhage. This study analyzed 141 SIAs (101 unruptured, 40 ruptured) using semi-automatic morphological analysis and high-resolution, image-based blood flow simulations from 3D rotational angiography. Advanced morphological and hemodynamic parameters were extracted, with clustering applied to address multicollinearity. Univariate logistic regression identified cluster representatives, and forward selection highlighted the maximum height, Neck inflow rate, and Non-sphericity index as rupture predictors, though only the latter two were significant. Clinical variables like age, sex, and comorbidities were also assessed but failed to predict rupture risk. The full model showed overfitting, with a pseudo-R2 of 0.142 on the training set but only 0.032 on the test set. A simplified model using just Neck inflow rate and Non-sphericity index performed similarly poorly (pseudo-R2 of 0.034). Multiple machine learning classifiers were evaluated, with similar performance across models, supporting the model-independence of the results. Overall, neither morphological, hemodynamic, nor clinical variables reliably predicted rupture risk, highlighting the limitations of current methods and underscoring the need for prospective studies and multimodal approaches that integrate imaging biomarkers and compare small and large aneurysms for better risk stratification. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-da89c50bf20e47629d2b3f89ebfe9af42025-08-20T03:45:23ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-08478-1Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm ruptureVanessa M. Swiatek0Samuel Voss1Florian Sprenger2Igor Fischer3Hafez Kader4Klaus-Peter Stein5Roland Schwab6Sylvia Saalfeld7Ali Rashidi8Daniel Behme9Philipp Berg10I. Erol Sandalcioglu11Belal Neyazi12Department of Neurosurgery, Otto-von-Guericke UniversityDepartment of Fluid Dynamics and Technical Flows, Otto-von-Guericke UniversityDepartment of Neurosurgery, Otto-von-Guericke UniversityDepartment of Neurosurgery, University Hospital Düsseldorf and Heinrich-Heine-UniversityAutonomous Multisensor Systems Group, Institute for Intelligent Cooperating Systems, Otto-von-Guericke UniversityDepartment of Neurosurgery, Otto-von-Guericke UniversityDepartment of Neuroradiology, Otto-von-Guericke UniversityForschungscampus STIMULATEDepartment of Neurosurgery, Otto-von-Guericke UniversityDepartment of Neuroradiology, Otto-von-Guericke UniversityDepartment of Medical Engineering, Otto-von-Guericke UniversityDepartment of Neurosurgery, Otto-von-Guericke UniversityDepartment of Neurosurgery, Otto-von-Guericke UniversityAbstract Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Rupture, though rare, can cause devastating subarachnoid hemorrhage. This study analyzed 141 SIAs (101 unruptured, 40 ruptured) using semi-automatic morphological analysis and high-resolution, image-based blood flow simulations from 3D rotational angiography. Advanced morphological and hemodynamic parameters were extracted, with clustering applied to address multicollinearity. Univariate logistic regression identified cluster representatives, and forward selection highlighted the maximum height, Neck inflow rate, and Non-sphericity index as rupture predictors, though only the latter two were significant. Clinical variables like age, sex, and comorbidities were also assessed but failed to predict rupture risk. The full model showed overfitting, with a pseudo-R2 of 0.142 on the training set but only 0.032 on the test set. A simplified model using just Neck inflow rate and Non-sphericity index performed similarly poorly (pseudo-R2 of 0.034). Multiple machine learning classifiers were evaluated, with similar performance across models, supporting the model-independence of the results. Overall, neither morphological, hemodynamic, nor clinical variables reliably predicted rupture risk, highlighting the limitations of current methods and underscoring the need for prospective studies and multimodal approaches that integrate imaging biomarkers and compare small and large aneurysms for better risk stratification.https://doi.org/10.1038/s41598-025-08478-1Small intracranial aneurysmsRupture risk assessmentSemiautomatic neck curve reconstructionComputational fluid dynamicsPredictive modeling |
| spellingShingle | Vanessa M. Swiatek Samuel Voss Florian Sprenger Igor Fischer Hafez Kader Klaus-Peter Stein Roland Schwab Sylvia Saalfeld Ali Rashidi Daniel Behme Philipp Berg I. Erol Sandalcioglu Belal Neyazi Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture Scientific Reports Small intracranial aneurysms Rupture risk assessment Semiautomatic neck curve reconstruction Computational fluid dynamics Predictive modeling |
| title | Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture |
| title_full | Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture |
| title_fullStr | Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture |
| title_full_unstemmed | Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture |
| title_short | Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture |
| title_sort | predictive modeling and machine learning show poor performance of clinical morphological and hemodynamic parameters for small intracranial aneurysm rupture |
| topic | Small intracranial aneurysms Rupture risk assessment Semiautomatic neck curve reconstruction Computational fluid dynamics Predictive modeling |
| url | https://doi.org/10.1038/s41598-025-08478-1 |
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