Initial Clinical Experience with AView—A Clinical Computational Platform for Intracranial Aneurysm Morphology, Hemodynamics, and Treatment Management

Xiang, Jianping; Varble, Nicole; Davies, Jason M.; Ansaar, T; Kono, Kenichi; Sugiyama, Shunsuke; Binning, Mandy J.; Tawk, Rabih G.; Choi, Hoon; Ringer, Andrew J.; Snyder, Kenneth V.; Levy, Elad I.; Hopkins, L. Nelson; Siddiqui, Adnan H.; Meng, Hui

doi:10.1016/j.wneu.2017.09.030

Cited by 15 publications

(13 citation statements)

References 27 publications

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“…Since hemodynamic or complex morphological information is currently not available for most aneurysms in a clinical setting, the web-interface will include the option of risk assessment for aneurysms with missing information based on reduced feature models [48]. At the same time, the presented work indicates that incorporating aneurysm risk assessment by means of hemodynamic and shape analysis is beneficial, which should be feasible in the long-term [49]. …”

Section: Discussionmentioning

confidence: 99%

Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics

et al. 2018

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“…Flow angle is another parameter that incorporates the relation of the aneurysm dome to the parent vessel, for which their spatial relationship has been shown to be an important determinant of flow patterns inside the aneurysm dome 20 , 45 . Merih et al .…”

Section: Discussionmentioning

confidence: 99%

Morphological parameters and anatomical locations associated with rupture status of small intracranial aneurysms

Duan

Guan

et al. 2018

Sci Rep

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Characterization of the rupture risk factors for small intracranial aneurysms (SIAs, ≤5 mm) is clinically valuable. The present study aims to identify image-based morphological parameters and anatomical locations associated with the rupture status of SIAs. Two hundred and sixty-three patients with single SIAs (128 ruptured, 135 unruptured) were included, and six morphological parameters, including size, aspect ratio (AR), size ratio (SR), height–width ratio (H/W), flow angle (FA) and aneurysm width–parent artery diameter ratio, and the aneurysm locations were evaluated using three-dimensional geometry, and were used to identify a correlation with aneurysm rupture. Statistically significant differences were observed between ruptured and unruptured groups for AR, SR, H/W, FA, and aneurysm locations, from univariate analyses. Logistic regression analysis further revealed that AR (p = 0.034), SR (p = 0.004), H/W (p = 0.003), and FA (p < 0.001) had the strongest independent correlation with ruptured SIAs after adjustment for age, gender and other clinical risk factors. A future study on a larger SIA cohort need to establish to what extent the AR, SR, H/W and FA increase the risk of rupture in patients with unruptured SIAs in terms of absolute risks.

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“…Third, to be able to apply such a model in a clinical setting, clinicians would need to be provided with a tool for image-based IA shape characterization as well as CFD simulations, which would seem to be feasible in the future. 24 Finally, the model could potentially be improved by taking into account additional patient-related information such as comorbidities or social habits including smoking or alcohol consumption.…”

Section: Clinical Considerationsmentioning

confidence: 99%

Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics

Detmer¹,

Hadad²,

Chung

et al. 2019

Neurosurgical Focus

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OBJECTIVEIncidental 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.METHODSPatient 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.RESULTSThe 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.CONCLUSIONSDeveloping 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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Initial Clinical Experience with AView—A Clinical Computational Platform for Intracranial Aneurysm Morphology, Hemodynamics, and Treatment Management

Cited by 15 publications

References 27 publications

Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics

Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics

Morphological parameters and anatomical locations associated with rupture status of small intracranial aneurysms

Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics

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