Determining Significant Morphological and Hemodynamic Parameters to Assess the Rupture Risk of Cerebral Aneurysms

  • Nicolás Amigo
  • Álvaro Valencia
Original Article


Hemodynamics and morphology are recognized as major factors in the rupture risk of cerebral aneurysms, and exploration of their relationship is necessary to establish a method that can be employed by clinicians to assess the likelihood of rupture. In this work, morphological analysis and computational fluid dynamics were carried out to examine a database of 58 lateral cerebral aneurysms (26 ruptured and 32 unruptured) distributed among 49 patients. Eight morphological and six hemodynamic parameters were calculated and evaluated for statistical significance. It was observed that size ratio (SR), systolic wall shear stress (SWSS), diastolic wall shear stress (DWSS) and relative residence time (RRT) were statistically significant. The SR, DWSS, SWSS, and RRT were employed in multivariate logistic regression, obtaining a combined morphological–hemodynamic model, a pure morphological model, and a pure hemodynamic model to evaluate the odds ratio for rupture risk. The combined model had the highest efficiency, but no distinctive difference existed in the predictive capacity of the three models.


Morphology Hemodynamic Cerebral aneurysm Rupture risk 



Computational fluid dynamics


Wall shear stress


Aspect ratio


Size ratio


Bottleneck factor


Nonsphericity index


Undulation index


Aneurysm angle


Flow angle


Vessel angle


Diastolic wall shear stress


Systolic wall shear stress


Time-averaged wall shear stress


Relative residence time


Oscillatory shear index


Aneurysm formation index


Receiver operating characteristics


Area under the curve



N. Amigo thanks CONICYT for PhD Fellowship CONICYT-PFCHA/Doctorado Nacional/2015-21151448. The authors would also like to thank Professor Ender Finol and Dr. Sourav Patnaik for their fruitful and constructive suggestions.


  1. 1.
    Brisman, J. L., Song, J. K., & Newell, D. W. (2006). Cerebral aneurysms. The New England Journal of Medicine, 355(9), 928–939.CrossRefGoogle Scholar
  2. 2.
    Wiebers, D. O. (2003). Unruptured intracranial aneurysms: Natural history, clinical outcome, and risks of surgical and endovascular treatment. The Lancet, 362(9378), 103–110.CrossRefGoogle Scholar
  3. 3.
    Dhar, S., Tremmel, M., Mocco, J., Kim, M., Yamamoto, J., Siddiqui, A. H., et al. (2008). Morphology parameters for intracranial aneurysm rupture risk assessment. Neurosurgery, 63(2), 185–197.CrossRefGoogle Scholar
  4. 4.
    Baharoglu, M. I., Schirmer, C. M., Hoit, D. A., Gao, B. L., & Malek, A. M. (2010). Aneurysm inflow-angle as a discriminant for rupture in sidewall cerebral aneurysms: Morphometric and computational fluid dynamic analysis. Stroke, 41(7), 1423–1430.CrossRefGoogle Scholar
  5. 5.
    Lin, N., Ho, A., Charoenvimolphan, N., Frerichs, K. U., Day, A. L., & Du, R. (2013). Analysis of morphological parameters to differentiate rupture status in anterior communicating artery aneurysms. PLoS ONE, 8(11), 1–8.Google Scholar
  6. 6.
    Huang, Z. Q., Meng, Z. H., Hou, Z. J., Huang, S. Q., Chen, J. N., Yu, H., et al. (2016). Geometric parameter analysis of ruptured and unruptured aneurysms in patients with symmetric bilateral intracranial aneurysms: A multicenter CT angiography study. American Journal of Neuroradiology, 37(8), 1413–1417.CrossRefGoogle Scholar
  7. 7.
    Cebral, J. R., Castro, M. A., Burgess, J. E., Pergolizzi, R. S., Sheridan, M. J., & Putman, C. M. (2005). Characterization of cerebral aneurysms for assessing risk of rupture by using patient-specific computational hemodynamics models. American Journal of Neuroradiology, 26(10), 2550–2559.Google Scholar
  8. 8.
    Valencia, A., Guzmán, A., Finol, E. A., & Amon, C. H. (2006). Blood flow dynamics in saccular aneurysm models of the basilar artery. Journal of Biomechanical Engineering, 128(4), 516–526.CrossRefGoogle Scholar
  9. 9.
    Xiang, J., Natarajan, S. K., Tremmel, M., Ma, D., Mocco, J., Hopkins, L. N., et al. (2011). Hemodynamic–morphologic discriminants for intracranial aneurysm rupture. Stroke, 42(1), 144–152.CrossRefGoogle Scholar
  10. 10.
    Jing, L., Fan, J., Wang, Y., Li, H., Wang, S., Yang, X., et al. (2015). Morphologic and hemodynamic analysis in the patients with multiple intracranial aneurysms: Ruptured versus unruptured. PLoS ONE, 10(7), 1–12.Google Scholar
  11. 11.
    Valencia, A., Morales, H., Rivera, R., Bravo, E., & Gálvez, M. (2008). Blood flow dynamics in patient-specific cerebral aneurysm models: The relationship between wall shear stress and aneurysm area index through a stenosis. Medical Engineering and Physics, 30(3), 329–340.CrossRefGoogle Scholar
  12. 12.
    Papanastasiou, T. C. (1987). Flows of materials with yield. Journal of Rheology, 31, 385–404.CrossRefzbMATHGoogle Scholar
  13. 13.
    Neofytou, P., & Drikakis, D. (2003). Effects of blood models on flows through a stenosis. The International Journal for Numerical Methods in Fluids, 43, 597–635.MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Valencia, A., Burdiles, P., Ignat, M., Mura, J., Bravo, E., Rivera, R., et al. (2013). Fluid structural analysis of human cerebral aneurysm using their own wall mechanical properties. Computational and Mathematical Methods in Medicine, 2013, 1–18.MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Raghavan, M. L., Ma, B., & Harbaugh, R. E. (2005). Quantified aneurysm shape and rupture risk. Journal of Neurosurgery, 102(2), 355–362.CrossRefGoogle Scholar
  16. 16.
    He, X., & Ku, D. N. (1996). Pulsatile flow in the human left coronary artery bifurcation: Average conditions. Journal of Biomechanical Engineering, 118(1), 74–82.CrossRefGoogle Scholar
  17. 17.
    Himburg, H. A., Grzybowski, D. M., Hazel, A. L., LaMack, J. A., Li, X. M., & Friedman, M. H. (2004). Spatial comparison between wall shear stress measures and porcine arterial endothelial permeability. American Journal of Physiology. Heart and Circulatory Physiology, 286, H1916–H1922.CrossRefGoogle Scholar
  18. 18.
    Lee, S. W., Antiga, L., & Steinman, D. A. (2009). Correlations among indicators of disturbed flow at the normal carotid bifurcation. Journal of Biomechanical Engineering, 131, 061013.CrossRefGoogle Scholar
  19. 19.
    Mantha, A., Karmonik, C., Benndorf, G., Strother, C., & Metcalfe, R. (2006). Hemodynamics in a cerebral artery before and after the formation of an aneurysm. American Journal of Neuroradiology, 27(1), 1113–1118.Google Scholar
  20. 20.
    Miura, Y., Ishida, F., Umeda, Y., Tanemura, H., Suzuki, H., Matsushima, S., et al. (2013). Low wall shear stress is independently associated with the rupture status of middle cerebral artery aneurysms. Stroke, 44(2), 519–521.CrossRefGoogle Scholar
  21. 21.
    Xu, J., Yu, Y., Wu, X., Wu, Y., Jiang, C., Wang, S., et al. (2013). Morphological and hemodynamic analysis of mirror posterior communicating artery aneurysms. PLoS ONE, 8(1), 1–7.Google Scholar
  22. 22.
    Xiang, J., Yu, J., Snyder, K. V., Levy, E. I., Siddiqui, A. H., & Meng, H. (2016). Hemodynamic–morphological discriminant models for intracranial aneurysm rupture remain stable with increasing sample size. Journal of Neurointerventional Surgery, 8(1), 104–110.CrossRefGoogle Scholar
  23. 23.
    Kashiwazaki, D., Kuroda, S., & Sapporo SAH Study Group. (2013). Size ratio can highly predict rupture risk in intracranial small (< 5 mm) aneurysms. Stroke, 44, 2169–2173.CrossRefGoogle Scholar
  24. 24.
    Qin, H., Yang, Q., Zhuang, Q., Long, J., Yang, F., & Zhang, H. (2017). Morphological and hemodynamic parameters for middle cerebral artery bifurcation aneurysm rupture risk assessment. Journal of Korean Neurosurgical Society, 60(5), 504–510.CrossRefGoogle Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2018

Authors and Affiliations

  1. 1.Departamento de Ingeniería MecánicaUniversidad de ChileSantiagoChile
  2. 2.Núcleo de Matemáticas, Física y Estadística, Facultad de CienciasUniversidad MayorSantiagoChile

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