Comparison of the unruptured intracranial aneurysm treatment score recommendations with clinical treatment results – A series of 322 aneurysms

Wende, Tim; Kasper, Johannes; Wilhemy, Florian; Prasse, Gordian; Quäschling, Ulf; Haase, Amelie; Meixensberger, Jürgen; Nestler, Ulf

doi:10.1016/j.jocn.2022.01.038

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“…Nevertheless, there is ongoing controversy regarding the suitability of each of those factors for risk prediction [8]. Based on those factors, a number of clinical scoring systems, such as the PHASES and UIATS score [9,10], have been developed with the aim to aid optimal decision making in neurovascular procedures. However, exemplarily related to the ethnic and biological differences between the patients treated in a local neurovascular center and the patients included in transcontinental multi-center studies, the appropriateness of those scores for the counseling of individual patients has been recently questioned [11,12].…”

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confidence: 99%

Machine Learning for Rupture Risk Prediction of Intracranial Aneurysms: Challenging the PHASES Score in Geographically Constrained Areas

et al. 2022

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Intracranial aneurysms represent a potentially life-threatening condition and occur in 3–5% of the population. They are increasingly diagnosed due to the broad application of cranial magnetic resonance imaging and computed tomography in the context of headaches, vertigo, and other unspecific symptoms. For each affected individual, it is utterly important to estimate the rupture risk of the respective aneurysm. However, clinically applied decision tools, such as the PHASES score, remain insufficient. Therefore, a machine learning approach assessing the rupture risk of intracranial aneurysms is proposed in our study. For training and evaluation of the algorithm, data from a single neurovascular center was used, comprising 446 aneurysms (221 ruptured, 225 unruptured). The machine learning model was then compared with the PHASES score and proved superior in accuracy (0.7825), F1-score (0.7975), sensitivity (0.8643), specificity (0.7022), positive predictive value (0.7403), negative predictive value (0.8404), and area under the curve (0.8639). The frequency distributions of the predicted rupture probabilities and the PHASES score were analyzed. A symmetry can be observed between the rupture probabilities, with a symmetry axis at 0.5. A feature importance analysis reveals that the body mass index, consumption of anticoagulants, and harboring vessel are regarded as the most important features when assessing the rupture risk. On the other hand, the size of the aneurysm, which is weighted most in the PHASES score, is regarded as less important. Based on our findings we discuss the potential role of the model for clinical practice in geographically confined aneurysm patients.

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