Evidence map›Paper›PMID 28649677›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2016

Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks.

Luyan Liu, Han Zhang, Islem Rekik, Xiaobo Chen, Qian Wang, Dinggang Shen

Abstract read
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In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
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

25 citing papers in PubMed.

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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

6 authors.

Luyan LiuMed-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Han ZhangDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Islem RekikDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Xiaobo ChenDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Qian WangMed-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Dinggang ShenDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Funding

Quantifying Brain Abnormality by Multimodality Neuroimage Analysis,R01AG041721 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIU, MINGXIA · 2012 to 2019
$3.0M
NIA NIH HHS R01 AG041721
6 · The paper itself

Abstract

High-grade glioma (HGG) is a lethal cancer, which is characterized by very poor prognosis. To help optimize treatment strategy, accurate preoperative prediction of HGG patient's outcome (i.e., survival time) is of great clinical value. However, there are huge individual variability of HGG, which produces a large variation in survival time, thus making prognostic prediction more challenging. Previous brain imaging-based outcome prediction studies relied only on the imaging intensity inside or slightly around the tumor, while ignoring any information that is located far away from the lesion (i.e., the "normal appearing" brain tissue). Notably, in addition to altering MR image intensity, we hypothesize that the HGG growth and its mass effect also change both structural (can be modeled by diffusion tensor imaging (DTI)) and functional brain connectivities (estimated by functional magnetic resonance imaging (rs-fMRI)). Therefore, integrating connectomics information in outcome prediction could improve prediction accuracy. To this end, we unprecedentedly devise a machine learning-based HGG prediction framework that can effectively extract valuable features from complex human brain connectome using network analysis tools, followed by a novel multi-stage feature selection strategy to single out good features while reducing feature redundancy. Ultimately, we use support vector machine (SVM) to classify HGG outcome as either

Indexed as

Machine LearningAlgorithmsBrainBrain NeoplasmsConnectomeDiffusion Tensor ImagingGliomaHumansLife ExpectancyMagnetic Resonance ImagingNeoplasm GradingReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityTreatment Outcome

Identifiers

PMID28649677
PMCPMC5479332

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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.