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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87165 |
| Date | 22 September 2023 |
| Creators | Walther, Georg, Martin, Christian, Haase, Amelie, Nestler, Ulf, Schob, Stefan |
| Publisher | MDPI |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 943, 10.3390/sym14050943 |
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