S F Salimi Ashkezari1, F Mut2, M Slawski3, C M Jimenez4, A M Robertson5,6, J R Cebral2,7. 1. From the Departments of Bioengineering (S.F.S.A., F.M., J.R.C.) ssalimia@gmu.edu. 2. From the Departments of Bioengineering (S.F.S.A., F.M., J.R.C.). 3. Statistics (M.S.). 4. Neurosurgery Department (C.M.J.), University of Antioquia, Medellin, Colombia. 5. Departments of Mechanical Engineering and Material Science (A.M.R.). 6. Bioengineering (A.M.R.), University of Pittsburgh, Pittsburgh, Pennsylvania. 7. Mechanical Engineering (J.R.C.), George Mason University, Fairfax, Virginia.
Abstract
BACKGROUND AND PURPOSE: Many small, regularly shaped cerebral aneurysms rupture; however, they usually receive a low score based on current risk-assessment methods. Our goal was to identify patient and aneurysm characteristics associated with rupture of small, regularly shaped aneurysms and to develop and validate predictive models of rupture in this aneurysm subpopulation. MATERIALS AND METHODS: Cross-sectional data from 1079 aneurysms smaller than 7 mm with regular shapes (without blebs) were used to train predictive models for aneurysm rupture using machine learning methods. These models were based on the patient population, aneurysm location, and hemodynamic and geometric characteristics derived from image-based computational fluid dynamics models. An independent data set with 102 small, regularly shaped aneurysms was used for validation. RESULTS: Adverse hemodynamic environments characterized by strong, concentrated inflow jets, high speed, complex and unstable flow patterns, and concentrated, oscillatory, and heterogeneous wall shear stress patterns were associated with rupture in small, regularly shaped aneurysms. Additionally, ruptured aneurysms were larger and more elongated than unruptured aneurysms in this subset. A total of 5 hemodynamic and 6 geometric parameters along with aneurysm location, multiplicity, and morphology, were used as predictive variables. The best machine learning rupture prediction-model achieved a good performance with an area under the curve of 0.84 on the external validation data set. CONCLUSIONS: This study demonstrated the potential of using predictive machine learning models based on aneurysm-specific hemodynamic, geometric, and anatomic characteristics for identifying small, regularly shaped aneurysms prone to rupture.
BACKGROUND AND PURPOSE: Many small, regularly shaped cerebral aneurysms rupture; however, they usually receive a low score based on current risk-assessment methods. Our goal was to identify patient and aneurysm characteristics associated with rupture of small, regularly shaped aneurysms and to develop and validate predictive models of rupture in this aneurysm subpopulation. MATERIALS AND METHODS: Cross-sectional data from 1079 aneurysms smaller than 7 mm with regular shapes (without blebs) were used to train predictive models for aneurysm rupture using machine learning methods. These models were based on the patient population, aneurysm location, and hemodynamic and geometric characteristics derived from image-based computational fluid dynamics models. An independent data set with 102 small, regularly shaped aneurysms was used for validation. RESULTS: Adverse hemodynamic environments characterized by strong, concentrated inflow jets, high speed, complex and unstable flow patterns, and concentrated, oscillatory, and heterogeneous wall shear stress patterns were associated with rupture in small, regularly shaped aneurysms. Additionally, ruptured aneurysms were larger and more elongated than unruptured aneurysms in this subset. A total of 5 hemodynamic and 6 geometric parameters along with aneurysm location, multiplicity, and morphology, were used as predictive variables. The best machine learning rupture prediction-model achieved a good performance with an area under the curve of 0.84 on the external validation data set. CONCLUSIONS: This study demonstrated the potential of using predictive machine learning models based on aneurysm-specific hemodynamic, geometric, and anatomic characteristics for identifying small, regularly shaped aneurysms prone to rupture.
Authors: Joel Björkman; Juhana Frösen; Olli Tähtinen; Daan Backes; Terhi Huttunen; Jaakko Harju; Jukka Huttunen; Mitja I Kurki; Mikael von Und Zu Fraunberg; Timo Koivisto; Hannu Manninen; Juha E Jääskeläinen; Antti E Lindgren Journal: Stroke Date: 2017-05-03 Impact factor: 7.914
Authors: Jacoba P Greving; Marieke J H Wermer; Robert D Brown; Akio Morita; Seppo Juvela; Masahiro Yonekura; Toshihiro Ishibashi; James C Torner; Takeo Nakayama; Gabriël J E Rinkel; Ale Algra Journal: Lancet Neurol Date: 2013-11-27 Impact factor: 44.182
Authors: Juan R Cebral; Fernando Mut; Piyusha Gade; Fangzhou Cheng; Yasutaka Tobe; Juhana Frosen; Anne M Robertson Journal: Int J Numer Method Biomed Eng Date: 2018-08-21 Impact factor: 2.747