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Felicitas J. Detmer, Sara Hadad, Bong Jae Chung, Fernando Mut, Martin Slawski, Norman Juchler, Vartan Kurtcuoglu, Sven Hirsch, Philippe Bijlenga, Yuya Uchiyama, Soichiro Fujimura, Makoto Yamamoto, Yuichi Murayama, Hiroyuki Takao, Timo Koivisto, Juhana Frösen and Juan R. Cebral


Incidental aneurysms pose a challenge for physicians, who need to weigh the rupture risk against the risks associated with treatment and its complications. A statistical model could potentially support such treatment decisions. A recently developed aneurysm rupture probability model performed well in the US data used for model training and in data from two European cohorts for external validation. Because Japanese and Finnish patients are known to have a higher aneurysm rupture risk, the authors’ goals in the present study were to evaluate this model using data from Japanese and Finnish patients and to compare it with new models trained with Finnish and Japanese data.


Patient and image data on 2129 aneurysms in 1472 patients were used. Of these aneurysm cases, 1631 had been collected mainly from US hospitals, 249 from European (other than Finnish) hospitals, 147 from Japanese hospitals, and 102 from Finnish hospitals. Computational fluid dynamics simulations and shape analyses were conducted to quantitatively characterize each aneurysm’s shape and hemodynamics. Next, the previously developed model’s discrimination was evaluated using the Finnish and Japanese data in terms of the area under the receiver operating characteristic curve (AUC). Models with and without interaction terms between patient population and aneurysm characteristics were trained and evaluated including data from all four cohorts obtained by repeatedly randomly splitting the data into training and test data.


The US model’s AUC was reduced to 0.70 and 0.72, respectively, in the Finnish and Japanese data compared to 0.82 and 0.86 in the European and US data. When training the model with Japanese and Finnish data, the average AUC increased only slightly for the Finnish sample (to 0.76 ± 0.16) and Finnish and Japanese cases combined (from 0.74 to 0.75 ± 0.14) and decreased for the Japanese data (to 0.66 ± 0.33). In models including interaction terms, the AUC in the Finnish and Japanese data combined increased significantly to 0.83 ± 0.10.


Developing an aneurysm rupture prediction model that applies to Japanese and Finnish aneurysms requires including data from these two cohorts for model training, as well as interaction terms between patient population and the other variables in the model. When including this information, the performance of such a model with Japanese and Finnish data is close to its performance with US or European data. These results suggest that population-specific differences determine how hemodynamics and shape associate with rupture risk in intracranial aneurysms.

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Philippe Bijlenga, Sandrine Morel, Sven Hirsch, Karl Schaller and Daniel Rüfenacht

The disease resulting in the formation, growth, and rupture of intracranial aneurysms is complex. Research is accumulating evidence that the disease is driven by many different factors, some constant and others variable over time. Combinations of factors may induce specific biophysical reactions at different stages of the disease. A better understanding of the biophysical mechanisms responsible for the disease initiation and progression is essential to predict the natural history of the disease. More accurate predictions are mandatory to adequately balance risks between observation and intervention at the individual level as expected in the age of personalized medicine. Multidisciplinary exploration of the disease also opens an avenue to the discovery of possible preventive actions or medical treatments. Modern information technologies and data processing methods offer tools to address such complex challenges requiring 1) the collection of a high volume of information provided globally, 2) integration and harmonization of the information, and 3) management of data sharing with a broad spectrum of stakeholders.

Over the last decade an infrastructure has been set up and is now made available to the academic community to support and promote exploration of intracranial disease, modeling, and clinical management simulation and monitoring.

The background and purpose of the infrastructure is reviewed. The infrastructure data flow architecture is presented. The basic concepts of disease modeling that oriented the design of the core information model are explained. Disease phases, milestones, cases stratification group in each phase, key relevant factors, and outcomes are defined. Data processing and disease model visualization tools are presented. Most relevant contributions to the literature resulting from the exploitation of the infrastructure are reviewed, and future perspectives are discussed.