Notice and Invitation
Oral Defense of Doctoral Dissertation
The Volgenau School of Engineering, George Mason University
Felicitas Josephine Detmer
Bachelor of Science, Karlsruhe Institute of Technology,
Master of Science, Otto von Guericke University Magdeburg,
Aneurysm Rupture Risk Analysis and Risk Prediction Modeling
Based on CFD
Simulations and Statistical Learning
Monday, September 23, 2019, 9:30am – 11:30am
Krasnow, Room 229
All are invited to attend.
Dr. Juan R. Cebral, Chair
Dr. Parag Chitnis
Dr. Fernando Mut
Dr. Martin Slawski
Cerebral aneurysms are a common vascular disease occurring in about 2-3%
of the general population. While most aneurysms remain asymptomatic and never rupture during a patient’s lifetime, aneurysm rupture leads to subarachnoid hemorrhage, a subtype of stroke, which is associated with high mortality, morbidity, and substantial economic
burden. Today, an increasing number of unruptured aneurysms are diagnosed as incidental findings. Since the risks related to treatment and post-treatment complications outweigh the relatively low natural aneurysm rupture risk, about 1% per patient per year
on average, the assessment of a patient’s individual aneurysm rupture risk is essential. The mechanisms leading to aneurysm rupture are, however, not fully understood, complicating the risk assessment. Different risk factors have been associated with aneurysm
rupture in previous studies, including hemodynamic, morphological, anatomical, and patient-related parameters. Combining such factors into a statistical model for predicting aneurysm rupture could assist physicians in their treatment decision of unruptured
aneurysms. Currently available models either do not include hemodynamic or morphological information, or are based on small sample sizes.
This dissertation addresses this current problem with the development
of statistical models for aneurysm rupture combining the different types of risk factors and using data of large patient cohorts with about 2,000 aneurysms for model development and evaluation. The models encompass a general aneurysm risk assessment model,
a specialized model for aneurysms at one particular location in the cerebral vasculature, models extended to different patient populations, and models taking the influence of variations of a patient’s blood flow on the aneurysm hemodynamics into account. Hemodynamic
parameters are particularly included in the analyses and models because of their role in aneurysm development, growth, and rupture through biomechanical signaling mechanisms in the vessel wall.
We will show that the combination of hemodynamic, morphological, anatomical,
and patient-related factors in a statistical model enables accurate prediction of aneurysm rupture status. Once evaluated with longitudinal data, translation of such a model into the clinic could support physicians in their treatment decisions of unruptured
aneurysms and potentially improve patient outcome.
Academic Program Assistant
Department of Bioengineering
3100 Peterson Family Health Services Hall