Literature DB >> 27728836

Discrepancy between structural and functional visual recovery in patients after trans-sphenoidal pituitary adenoma resection.

Nidan Qiao1, Zhao Ye1, Xuefei Shou1, Yongfei Wang1, Shiqi Li1, Min Wang2, Yao Zhao3.   

Abstract

OBJECTIVE: The relationship between functional and structural measurements is of fundamental importance in monitoring treatment and progression in patients with pituitary adenoma. In the present study, we examined the association between longitudinal changes in standard automated perimetry (SAP), retinal nerve fiber layer (RNFL) thickness and multifocal visual evoked potential (mfVEP) amplitude after transsphenoidal surgery.
METHODS: Thirty patients with pituitary adenoma were recruited from Huashan Hospital between September 2010 and January 2014. The examination included pupil examination, anterior and posterior segment examination, SAP, RNFL and mfVEP. At three months and nine months after transsphenoid surgery, follow-up measurements were conducted in twenty-three patients, and at 18 months after surgery, the same examinations were performed in seven patients.
RESULTS: The average age of patients was 42.6±12.1years, with 23 males and 7 females. The mean score of SAP improved significantly: 1.75 before surgery; 0.62 at three months after surgery (p=0.00) and 0.50 at nine months after surgery (p=0.00). No significant improvement in RNFL thickness was observed at three months or nine months after surgery. The mean score of mfVEP also improved significantly: 0.85 before surgery; 0.53 at three months (p=0.00) and 0.38 at nine months after surgery (P=0.00). No statistical difference was observed in the outcome of patients at nine months of follow-up and 18 months of follow-up.
CONCLUSION: Visual field and mfVEP recovery with unchanged RNFL thickness was observed in patients after transsphenoid pituitary adenoma resection. Copyright Â
© 2016. Published by Elsevier B.V.

Entities:  

Keywords:  Mutifocal visual evoked potential; Recovery; Retinal nerve fiber layer; Visual field

Mesh:

Year:  2016        PMID: 27728836     DOI: 10.1016/j.clineuro.2016.09.005

Source DB:  PubMed          Journal:  Clin Neurol Neurosurg        ISSN: 0303-8467            Impact factor:   1.876


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3.  Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors.

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