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A Classification for the Anterior Inferior Cerebellar Artery-Subarcuate Artery Complex Based on the Embryological Development.

Jorge RasmussenPedro PlouÁlvaro CamperoPablo Ajler
Published in: Journal of neurological surgery. Part B, Skull base (2019)
Objective  To hierarchize the anterior inferior cerebellar artery (AICA)-subarcuate artery (SAA) complex's variations in the surgical field. Background  The AICA's "subarcuate loop" (SL) presents multiple variations, closely related to the SAA. AICA-SAA complex's variations may represent major issues in cerebellopontine angle (CPA) surgery. As the spectrum of configurations is originated during the development, a systematized classification was proposed based on the interaction between the petrosal bone and the AICA in the embryonic period. Methods  The variations were defined as follow: Grade 0: free, purely cisternal AICA, unidentifiable or absent SAA; Grade 1: purely cisternal AICA, loose SL, SAA > 3 mm; Grade 2: AICA near the subarcuate fossa, pronounced SL, SAA <3 mm; Grade 3: "duralized" AICA, unidentifiable SAA, or included in the petromastoid canal (PMC); and Grade 4: intraosseous AICA, unidentifiable SAA, or included in the PMC. The classification was applied to a series of patients assessed by magnetic resonance constructive interference in steady state sequence. Surgical examples were also provided. Results  Eighty-four patients were evaluated, including 161 CPA. The proportions found in the gradation remained within the range of previous publications (Grade 0: 42.2%; Grade 1: 11.2%; Grade 2: 35.4%; Grade 3: 10.6%; and Grade 4: 0.6%). Moreover, the degrees of the classification were related to the complexity of the anatomical relationships and, therefore, to the difficulty of the maneuvers required to overcome them. Conclusion  The proposed AICA-SAA complex classification allowed to distinguish and objectify pre- and intraoperatively the spectrum of variations, to thoroughly plan the required actions and instrumentation.
Keyphrases
  • machine learning
  • deep learning
  • magnetic resonance
  • newly diagnosed
  • prognostic factors
  • high resolution
  • body composition
  • chronic kidney disease
  • atrial fibrillation
  • contrast enhanced