Objective We have previously developed three logistic regression models for discriminating intracranial aneurysm rupture status from 119 aneurysms based on hemodynamic–morphological parameters. In this study we exploit their use as a tool for predicting the risk of rupture of aneurysms with a defined Rupture Resemblance Score (RRS).

Methods We collected three-dimensional images of 85 consecutive aneurysms, applied the three regression models and compared model performance at predicting rupture status against anecdotal metrics (aneurysm size and aspect ratio). We then reinterpreted the model-predicted probability as RRS, where the higher the score the closer the resemblance to previously known rupture components, and applied the RRS prospectively to four unruptured aneurysms with borderline treatment decisions.

Results All three models yielded excellent sensitivity (0.78–0.83) and specificity (0.78–0.84) at a cutoff score of 50%, whereas aneurysm size and aspect ratio showed poor sensitivities (0.28 and 0.33, respectively). Lowering the cutoff score to 30% improved sensitivity to 0.90. The RRS identified most of the ruptured aneurysms and also some unruptured ones that closely resembled ruptured aneurysms hemodynamically and/or morphologically. The prospective application of the RRS to unruptured aneurysms shows that it could provide additional insights for treatment decisions.

Conclusions Previous regression models based on hemodynamic–morphological parameters are able to discriminate rupture in a new cohort in the same population. A higher probability of rupture is associated with larger size ratio, lower normalized wall shear stress and higher oscillatory shear index. The RRS could potentially stratify rupture risk and assist in treatment decision-making for unruptured aneurysms.