Literature DB >> 27885957

Visual pathway impairment by pituitary adenomas: quantitative diagnostics by diffusion tensor imaging.

Ylva Lilja1,2, Oscar Gustafsson3,4, Maria Ljungberg3,4, Göran Starck3,4, Bertil Lindblom5, Thomas Skoglund1,6, Henrik Bergquist7,2, Karl-Erik Jakobsson1,6, Daniel Nilsson1,6.   

Abstract

OBJECTIVE Despite ample experience in surgical treatment of pituitary adenomas, little is known about objective indices that may reveal risk of visual impairment caused by tumor growth that leads to compression of the anterior visual pathways. This study aimed to explore diffusion tensor imaging (DTI) as a means for objective assessment of injury to the anterior visual pathways caused by pituitary adenomas. METHODS Twenty-three patients with pituitary adenomas, scheduled for transsphenoidal tumor resection, and 20 healthy control subjects were included in the study. A minimum suprasellar tumor extension of Grade 2-4, according to the SIPAP (suprasellar, infrasellar, parasellar, anterior, and posterior) scale, was required for inclusion. Neuroophthalmological examinations, conventional MRI, and DTI were completed in all subjects and were repeated 6 months after surgery. Quantitative assessment of chiasmal lift, visual field defect (VFD), and DTI parameters from the optic tracts was performed. Linear correlations, group comparisons, and prediction models were done in controls and patients. RESULTS Both the degree of VFD and chiasmal lift were significantly correlated with the radial diffusivity (r = 0.55, p < 0.05 and r = 0.48, p < 0.05, respectively) and the fractional anisotropy (r = -0.58, p < 0.05 and r = -0.47, p < 0.05, respectively) but not with the axial diffusivity. The axial diffusivity differed significantly between controls and patients with VFD, both before and after surgery (p < 0.05); however, no difference was found between patients with and without VFD. Based on the axial diffusivity and fractional anisotropy, a prediction model classified all patients with VFD correctly (sensitivity 1.0), 9 of 12 patients without VFD correctly (sensitivity 0.75), and 17 of 20 controls as controls (specificity 0.85). CONCLUSIONS DTI could detect pathology and degree of injury in the anterior visual pathways that were compressed by pituitary adenomas. The correlation between radial diffusivity and visual impairment may reflect a gradual demyelination in the visual pathways caused by an increased tumor effect. The low level of axial diffusivity found in the patient group may represent early atrophy in the visual pathways, detectable on DTI but not by conventional methods. DTI may provide objective data, detect early signs of injury, and be an additional diagnostic tool for determining indication for surgery in cases of pituitary adenomas.

Entities:  

Keywords:  AD = axial diffusivity; DTI = diffusion tensor imaging; FA = fractional anisotropy; HRP = high-pass resolution perimetry; MD = mean diffusivity; OT = optic tract; RD = radial diffusivity; ROI = region of interest; SIPAP = suprasellar, infrasellar, parasellar, anterior, and posterior; VFD = visual field defect; anterior visual pathways; diffusion tensor imaging; pituitary adenoma; pituitary surgery; quantitative diagnostics, diagnostic technique; visual impairment

Mesh:

Year:  2016        PMID: 27885957     DOI: 10.3171/2016.8.JNS161290

Source DB:  PubMed          Journal:  J Neurosurg        ISSN: 0022-3085            Impact factor:   5.115


  6 in total

1.  Quantitative and functional visual field outcomes after endoscopic trans-sphenoidal pituitary adenectomy.

Authors:  Dhruv Parikh; James M W Robins; Tess Garretty; Asim J Sheikh; Atul K Tyagi; Paul A Nix; Nick I Phillips
Journal:  Acta Neurochir (Wien)       Date:  2022-04-15       Impact factor: 2.216

Review 2.  Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.

Authors:  Kelvin Koong; Veronica Preda; Anne Jian; Benoit Liquet-Weiland; Antonio Di Ieva
Journal:  Neuroradiology       Date:  2021-11-27       Impact factor: 2.804

Review 3.  Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions.

Authors:  Ashirbani Saha; Samantha Tso; Jessica Rabski; Alireza Sadeghian; Michael D Cusimano
Journal:  Pituitary       Date:  2020-06       Impact factor: 4.107

4.  Quantitative assessment of secondary white matter injury in the visual pathway by pituitary adenomas: a multimodal study at 7-Tesla MRI.

Authors:  John W Rutland; Francesco Padormo; Cindi K Yim; Amy Yao; Annie Arrighi-Allisan; Kuang-Han Huang; Hung-Mo Lin; James Chelnis; Bradley N Delman; Raj K Shrivastava; Priti Balchandani
Journal:  J Neurosurg       Date:  2019-01-18       Impact factor: 5.115

5.  Primary visual cortical thickness in correlation with visual field defects in patients with pituitary macroadenomas: a structural 7-Tesla retinotopic analysis.

Authors:  John W Rutland; Bradley N Delman; Kuang-Han Huang; Gaurav Verma; Noah C Benson; Dillan F Villavisanis; Hung-Mo Lin; Joshua B Bederson; James Chelnis; Raj K Shrivastava; Priti Balchandani
Journal:  J Neurosurg       Date:  2019-10-18       Impact factor: 5.115

6.  A pilot study of combined optical coherence tomography and diffusion tensor imaging method for evaluating microstructural change in the visual pathway of pituitary adenoma patients.

Authors:  Yanhua Pang; Zhi Tan; Wei Mo; Xinxin Chen; Jinfen Wei; Qing Guo; Qin Zhong; Jingxiang Zhong
Journal:  BMC Ophthalmol       Date:  2022-03-12       Impact factor: 2.209

  6 in total

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