Predicting Arachnoid Membrane Descent in the Chiasmatic Cistern in the Treatment of Pituitary Macroadenoma.
J Korean Neurosurg Soc
; 64(1): 110-119, 2021 Jan.
Article
em En
| MEDLINE
| ID: mdl-33105530
ABSTRACT
OBJECTIVE:
Preoperative prediction of the arachnoid membrane descent in pituitary surgery is useful for achieving gross total removal and avoiding cerebrospinal fluid leakage resulting from tearing of the arachnoid membrane in the chiasmatic cistern. In this study, we analyzed the patterns of arachnoid membrane descent during or after pituitary tumor surgery and identified the factors related to this descent.METHODS:
Analysis was restricted to pituitary macroadenomas not extending into the third ventricle or over the internal carotid artery. To minimize confounding factors, patients who underwent revision surgery, those who had a torn arachnoid during operation or small medial diaphragma sellae (DS) opening, and subtotal resections were excluded. We enrolled 41 consecutive patients in this retrospective analysis. The degree of arachnoid descent was categorized using intraoperative videos. Preoperative magnetic resonance findings, including tumor height, suprasellar extension, and variables including DS area and medial opening size, tumor composition, and displacement of the pituitary stalk and gland were evaluated to determine their correlations with arachnoid membrane descent.RESULTS:
Arachnoid membrane descent was significantly correlated with DS area and medial opening size. Based on T2-weighted images (T2WI) magnetic resonance (MR) images, tumor composition was significantly associated with arachnoid membrane descent. Other factors were not significantly correlated with arachnoid membrane descent.CONCLUSION:
T2WI of tumor composition and preoperative MR imaging of DS area and medial opening provided valuable information regarding arachnoid membrane descent. These parameters may serve as fundamental measures to facilitate complete resection of pituitary macroadenomas.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article