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Neurosurgery. 2014 Mar;10 Suppl 1:145-53; discussion 153-5. doi: 10.1227/NEU.0000000000000250.

Middle cerebral artery bifurcation aneurysms: an anatomic classification scheme for planning optimal surgical strategies.

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1
*Department of Neurological Surgery, and ‡Department of Computer Science and Engineering, Washington University in St. Louis, Missouri.

Abstract

BACKGROUND:

Changing landscapes in neurosurgical training and increasing use of endovascular therapy have led to decreasing exposure in open cerebrovascular neurosurgery. To ensure the effective transition of medical students into competent practitioners, new training paradigms must be developed.

OBJECTIVE:

Using principles of pattern recognition, we created a classification scheme for middle cerebral artery (MCA) bifurcation aneurysms that allows their categorization into a small number of shape pattern groups.

METHODS:

Angiographic data from patients with MCA aneurysms between 1995 and 2012 were used to construct 3-dimensional models. Models were then analyzed and compared objectively by assessing the relationship between the aneurysm sac, parent vessel, and branch vessels. Aneurysms were then grouped on the basis of the similarity of their shape patterns in such a way that the in-class similarities were maximized while the total number of categories was minimized. For each category, a proposed clip strategy was developed.

RESULTS:

From the analysis of 61 MCA bifurcation aneurysms, 4 shape pattern categories were created that allowed the classification of 56 aneurysms (91.8%). The number of aneurysms allotted to each shape cluster was 10 (16.4%) in category 1, 24 (39.3%) in category 2, 7 (11.5%) in category 3, and 15 (24.6%) in category 4.

CONCLUSION:

This study demonstrates that through the use of anatomic visual cues, MCA bifurcation aneurysms can be grouped into a small number of shape patterns with an associated clip solution. Implementing these principles within current neurosurgery training paradigms can provide a tool that allows more efficient transition from novice to cerebrovascular expert.

PMID:
24226424
DOI:
10.1227/NEU.0000000000000250
[Indexed for MEDLINE]
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