Jianping Xiang1, Nicole Varble2, Jason M Davies3, Ansaar T Rai4, Kenichi Kono5, Shin-Ichiro Sugiyama6, Mandy J Binning7, Rabih G Tawk8, Hoon Choi9, Andrew J Ringer10, Kenneth V Snyder11, Elad I Levy11, L Nelson Hopkins11, Adnan H Siddiqui11, Hui Meng12. 1. Toshiba Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, State University of New York, Buffalo, New York, USA. 2. Toshiba Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, New York, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA. 3. Department of Neurosurgery, University at Buffalo, State University of New York, Buffalo, New York, USA; Department of Biomedical Informatics, University at Buffalo, State University of New York, Buffalo, New York, USA. 4. Department of Neurosurgery, West Virginia University, Morgantown, West Virginia, USA. 5. Department of Neurosurgery, Wakayama Rosai Hospital, Wakayama, Japan. 6. Department of Neurosurgery, Tohoku University, Sendai, Miyagi, Japan. 7. Capital Institute of Neurosciences, Capital Health Systems, Trenton, New Jersey, USA. 8. Department of Neurosurgery, Mayo Clinic, Jacksonville, Florida, USA. 9. Department of Neurosurgery, SUNY Upstate University Hospital, Syracuse, New York, USA. 10. Department of Neurosurgery, Mayfield Clinic, TriHealth Health System, Cincinnati, Ohio, USA. 11. Toshiba Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, State University of New York, Buffalo, New York, USA; Department of Radiology, University at Buffalo, State University of New York, Buffalo, New York, USA. 12. Toshiba Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, State University of New York, Buffalo, New York, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA; Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA. Electronic address: huimeng@buffalo.edu.
Abstract
BACKGROUND: The management of intracranial aneurysm (IA) is challenging. Clinicians often rely on varied and intuitively disparate ways of evaluating rupture risk that may only partially take into account complex hemodynamic and morphologic factors. We developed a prototype of a clinically oriented, streamlined, computational platform, AView, for rapid assessment of hemodynamics and morphometrics in clinical settings. To show the potential clinical utility of AView, we report our initial multicenter experience highlighting the possible advantages of morphologic and hemodynamic analysis of IAs. METHODS: AView software was deployed across 8 medical centers (6 in the United States, 2 in Japan). Eight clinicians were trained and used the AView software between September 2012 and January 2013. RESULTS: We present 12 illustrative cases that show the potential clinical utility of AView. For all, morphology and hemodynamics, flow visualization, and rupture resemblance score (a surrogate for rupture risk) were provided. In 3 cases, AView could confirm the clinicians' decision to treat; in 3 cases, it could suggest which aneurysms may be at greater risk among multiple aneurysms; in 5 cases, AView could provide additional information for use during treatment decisions for ambiguous situations. In one stent-assisted coiling case, flow visualization predicted that the intuitive choice for stent placement could have resulted in sacrifice of an anterior cerebral artery due to blockage by coils and led clinicians to reconsider treatment plans. CONCLUSIONS: AView has the potential to confirm decisions to treat IAs, suggest which among multiple aneurysms to treat, and guide treatment decisions. Furthermore, the flow visualization it affords can inform aneurysm treatment planning and potentially avoid poor outcomes.
BACKGROUND: The management of intracranial aneurysm (IA) is challenging. Clinicians often rely on varied and intuitively disparate ways of evaluating rupture risk that may only partially take into account complex hemodynamic and morphologic factors. We developed a prototype of a clinically oriented, streamlined, computational platform, AView, for rapid assessment of hemodynamics and morphometrics in clinical settings. To show the potential clinical utility of AView, we report our initial multicenter experience highlighting the possible advantages of morphologic and hemodynamic analysis of IAs. METHODS: AView software was deployed across 8 medical centers (6 in the United States, 2 in Japan). Eight clinicians were trained and used the AView software between September 2012 and January 2013. RESULTS: We present 12 illustrative cases that show the potential clinical utility of AView. For all, morphology and hemodynamics, flow visualization, and rupture resemblance score (a surrogate for rupture risk) were provided. In 3 cases, AView could confirm the clinicians' decision to treat; in 3 cases, it could suggest which aneurysms may be at greater risk among multiple aneurysms; in 5 cases, AView could provide additional information for use during treatment decisions for ambiguous situations. In one stent-assisted coiling case, flow visualization predicted that the intuitive choice for stent placement could have resulted in sacrifice of an anterior cerebral artery due to blockage by coils and led clinicians to reconsider treatment plans. CONCLUSIONS: AView has the potential to confirm decisions to treat IAs, suggest which among multiple aneurysms to treat, and guide treatment decisions. Furthermore, the flow visualization it affords can inform aneurysm treatment planning and potentially avoid poor outcomes.
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