Evidence map›Paper›PMID 33893857›Full record

ArticleEuropean radiology2021

Quantitative visual pathway abnormalities predict visual field defects in patients with pituitary adenomas: a diffusion spectrum imaging study.

Lihong Liang, Hai Lin, Fan Lin, Jihu Yang, Hanwen Zhang, Liang Zeng, Yaqiong Hu, Weiwu Lan, Hua Zhong, Hong Zhang and 4 more

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In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 2 institutions in 1 country.

Lihong Liang *Department of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Hai Lin *Shenzhen University School of Medicine, Shenzhen, Guangdong, China.
Fan LinDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China. foxetfoxet@gmail.com.
Jihu YangShenzhen University School of Medicine, Shenzhen, Guangdong, China.
Hanwen ZhangDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Liang ZengDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Yaqiong HuDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Weiwu LanDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Hua ZhongDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Hong ZhangDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Siping LuoDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Yongqian MoDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Weihua LiDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China.
Yi LeiDepartment of Radiology, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, Guangdong, China. leiyisz2011@163.com.
Shenzhen University · CNShenzhen Second People's Hospital · CN

Funding

The Basic Plan Program of Shenzhen JCYJ20180228163333734The Guangdong Basic and Applied Basic Research Foundation 2019A1515111106The Subject Construction Program of Shenzhen SZXJ2018056
6 · The paper itself

Abstract

objectivesThis study was to investigate clinical applicability of diffusion spectrum imaging (DSI) for quantitative detection of visual pathway abnormalities to predict the degree of visual field defects (VFD) in patients with pituitary adenomas.

methodsSixty-five patients with pituitary adenomas and 33 healthy controls underwent conventional MRI and DSI scanning that allowed high-angular-resolution fiber tracking. Optic chiasmal compression and VFD were confirmed in all patients via radiological and neuro-ophthalmological examinations. Quantitative assessments of chiasmal lift, VFD, and DSI parameters from the optic nerve, optic tract, and optic radiation were performed. Group comparisons and correlation analyses were conducted in patients and controls. Using the 5-fold cross-validation method, the support vector machine classifiers were constructed to predict the degree of visual defects.

resultsThe mean values of quantitative anisotropy and generalized fractional anisotropy in optic nerve and optic tract showed significant differences between patients and controls (p < 0.05). These parameters were also significantly correlated with the chiasmal lift distance and degree of visual defects (p < 0.05). All patients were divided into mild (n = 42) and severe (n = 23) VFD groups, using the mean deviation value of -8 dB as the threshold. The classifiers achieved an accuracy of 0.83, sensitivity of 0.78, and specificity of 0.86 to discriminate patients with mild and severe visual defects.

conclusionsUsing high-angular-resolution fiber tracking, DSI may provide quantitative information to detect visual pathway abnormalities and be a potential diagnostic tool for determining the degree of visual field defects in pituitary adenomas. KEY POINTS: • Abnormal QA and GFA values of optic nerve and optic tract in adenoma patients • Close relationship between DSI parameters and VFD degree in adenoma patients • The classifiers achieved an accuracy of 0.83, sensitivity of 0.78, and specificity of 0.86 to discriminate patients with mild and severe VFD.

Indexed as

AdenomaPituitary NeoplasmsHumansVisual FieldsVisual Field TestsVisual PathwaysAdenomaDiffusion magnetic resonance imagingSupport vector machineVisual fieldsVisual pathway

Identifiers

PMID33893857
OpenAlexW3152529685

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.