Evidence map›Paper›PMID 30425818›Full record

ArticleJournal of healthcare engineering2018

Rank-Two NMF Clustering for Glioblastoma Characterization.

Aymen Bougacha, Ines Njeh, Jihene Boughariou, Omar Kammoun, Kheireddine Ben Mahfoudh, Mariem Dammak, Chokri Mhiri, Ahmed Ben Hamida

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of healthcare engineering, 2018. 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
–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

0 citing papers in PubMed.

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

8 authors.

Aymen BougachaATMS-ENIS, Advanced Technologies for Medicine and Signals, Department of Electrical and Computer Engineering, National Engineers School, Sfax University, Sfax, Tunisia.ORCID 0000-0001-7511-1424
Ines NjehATMS-ENIS, Advanced Technologies for Medicine and Signals, Department of Electrical and Computer Engineering, National Engineers School, Sfax University, Sfax, Tunisia.
Jihene BoughariouATMS-ENIS, Advanced Technologies for Medicine and Signals, Department of Electrical and Computer Engineering, National Engineers School, Sfax University, Sfax, Tunisia.
Omar KammounDepartment of Radiology, University Hospital Habib Bourguiba, Sfax, Tunisia.
Kheireddine Ben MahfoudhDepartment of Radiology, University Hospital Habib Bourguiba, Sfax, Tunisia.
Mariem DammakDepartment of Neurology, University Hospital Habib Bourguiba, Sfax, Tunisia.
Chokri MhiriDepartment of Neurology, University Hospital Habib Bourguiba, Sfax, Tunisia.
Ahmed Ben HamidaATMS-ENIS, Advanced Technologies for Medicine and Signals, Department of Electrical and Computer Engineering, National Engineers School, Sfax University, Sfax, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates a novel classification method for 3D multimodal MRI glioblastomas tumor characterization. We formulate our segmentation problem as a linear mixture model (LMM). Thus, we provide a nonnegative matrix

Indexed as

AlgorithmsBrain NeoplasmsCluster AnalysisGlioblastomaHumansImage Interpretation, Computer-AssistedMagnetic Resonance Imaging

Identifiers

PMID30425818
PMCPMC6218733

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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.