Susceptibility-Weighted Imaging of Deep Venous Congestion in Petroclival Meningioma

Kazuhide Adachi, Mituhiro Hasegawa, Motoharu Hayakawa, Shinichiro Tateyama, Yuichi Hirose

Research output: Contribution to journalArticle

Abstract

Background: Protecting the venous drainage route during surgery in cases of petroclival meningioma (PCM) is important. Identifying venous congestion preoperatively can be valuable in reducing the risks associated with venous congestion during surgery. In this study, we examined the utility of susceptibility-weighted imaging (SWI) in identifying the presence of venous congestion in PCM cases preoperatively and identified the factors associated with it. Methods: We retrospectively examined 24 patients who had undergone surgery for primary PCM. The areas of the basal and internal cerebral veins on the affected and unaffected sides, obtained using SWI, were compared to identify venous congestion. We further examined the association between multiple candidate factors that are thought to be related to venous congestion and venous congestion using statistical analyses. Results: SWI could successfully identify venous congestion in 11 of 24 PCM cases. Among the 12 factors examined, those associated with venous congestion were an extension of the tumor, over the midline or upward, which is known to disturb the venous flow at the brainstem surface; anastomosis of the superficial cerebral vein (i.e., bypass route for venous congestion); and a high ABC Surgical Risk Scale score, an indicator of postoperative neurologic deterioration. Conclusions: We showed that SWI is useful for evaluating venous congestion in PCM cases preoperatively and for identifying factors reflecting the risk of venous congestion. Taken together, our findings provide a multimodal strategy for the preoperative prediction of venous congestion, which could facilitate the treatment of PCM.

Original languageEnglish
Pages (from-to)e20-e31
JournalWorld Neurosurgery
Volume122
DOIs
Publication statusPublished - 02-2019

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All Science Journal Classification (ASJC) codes

  • Surgery
  • Clinical Neurology

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