Evidence map›Paper›PMID 35064370›Full record

ArticleJournal of digital imaging2022

Optimized Tractography Mapping and Quantitative Evaluation of Pyramidal Tracts for Surgical Resection of Insular Gliomas: a Correlative Study with Diffusion Tensor Imaging-Derived Metrics and Patient Motor Strength.

Ye Li, Yuanzheng Hou, Qiongge Li, Jie Tang, Jie Lu

Abstract read
In one paragraph

Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ye LiDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China.
Yuanzheng HouDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China.
Qiongge LiDepartment of Radiology, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China.
Jie TangDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China. tangjiett@163.com.
Jie LuDepartment of Radiology, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China. imaginglu@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigate the correlation between diffusion tensor imaging (DTI)-derived metric statistics and motor strength grade of insular glioma patients after optimizing the pyramidal tract (PT) delineation. Motor strength grades of 45 insular glioma patients were assessed. All the patients underwent structural and diffusion MRI examination before and after surgery. We co-registered pre- and post-op datasets, and a two-tensor unscented Kalman filter (UKF) algorithm was employed to delineate bilateral PTs after DWI pre-processing. The tractography results were voxelized, and their labelmaps were cropped according to the location of frontal and insular parts of the lesion. Both the whole and cropped labelmaps were used as regions of interest to analyze fractional anisotropy (FA) and Trace statistics; hence, their ratios were calculated (lesional side tract/contralateral normal tract). The combination of DWI pre-processing and two-tensor UKF algorithm successfully delineated bilateral PTs of all the patients. It effectively accomplished both full fiber delineation within the edema and an extensive lateral fanning that had a favorable correspondence to the bilateral motor cortices. Before surgery, correlations were found between patients' motor strength grades and ratios of PT volume and FA standard deviation (SD). Nearly 3 months after surgery, correlations were found between motor strength grades and the ratios of metric statistics as follows: whole PT volume, whole mean FA, and FA SD. We substantiated the correlation between DTI-derived metric statistics and motor strength grades of insular glioma patients. Moreover, we posed a workflow for comprehensive pre- and post-op DTI quantitative research of glioma patients.

Indexed as

Brain NeoplasmsGliomaDiffusion Tensor ImagingHumansPyramidal Tracts3D SlicerDiffusion tensor imagingInsular gliomaPyramidal tracts

Identifiers

PMID35064370
PMCPMC8921407

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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.