Evidence map›Paper›PMID 35800828›Full record

ArticleCureus2022

Comparative Quantification of Diffusion Tensor Tractography Using Automated Whole Brain MRI Tractography for Intracranial Tumor Surgery: Technical Note.

Cindy Alms, Chikezie I Eseonu

Open access · diamondAbstract read
In one paragraph

Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 55% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 1 institution in 1 country.

Cindy AlmsNeurological Surgery, University of Pittsburgh Medical Center (UPMC) Central Pennsylvania, Harrisburg, USA.
Chikezie I EseonuNeurological Surgery, University of Pittsburgh Medical Center (UPMC) Central Pennsylvania, Harrisburg, USA.
Neurological Surgery · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the improvement of diffusion tensor imaging (DTI) and algorithms, diffusion tensor tractography (DTT) may provide quantitative information on white matter tracts (WMT) that may help quantitatively assess WMT integrity and distortion, which may help with correlations of neurologic function or prognosis. This manuscript is the first to describe a technical method for quantitative analysis of clinically relevant white matter tracts during intracranial tumor surgery. The authors quantitatively analyzed relevant proximal WMT, pre and postoperatively, in a patient undergoing cranial surgery using DTT software to evaluate fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), geodesic anisotropy (GA), tract count, and tract volume. A method was then established to formulate quantitative comparisons between pre and postoperative WMT. Quantitative assessment of the corticospinal and optic radiation tracts revealed significant increases in the FA, GA, and tract count in the corticospinal and optic radiations postoperatively (p<.0001). MD, RD, and AD were found to be significantly diminished postoperatively (p<.0001). The postoperative optic radiations showed diminished volume as a result of damage to the tract pathway. To conclude, the utilization of white matter tractography provides a technical advancement that allows for quantitative comparative assessments of white matter tracts, which could assess the degree of brain changes following tumor surgery.

Indexed as

brain tumorcylindrical retractiondiffusion tensor imagingdiffusion tensor tractographywhole brain tractography

Identifiers

PMID35800828
PMCPMC9246502
OpenAlexW4281757933

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.