Evidence map›Paper›PMID 29542059›Full record

ArticleJournal of neuro-oncology2018

Malignancy probability map as a novel imaging biomarker to predict malignancy distribution: employing MRS in GBM patients.

Manijeh Beigi, Kevan Ghasemi, Parvin Mirzaghavami, Mohammadreza Khanmohammadi, Hamidreza SalighehRad

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In one paragraph

Article in Journal of neuro-oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Manijeh BeigiQuantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Institute for Advanced Medical Imaging, Tehran University of Medical Sciences, Tehran, Iran.
Kevan GhasemiQuantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Institute for Advanced Medical Imaging, Tehran University of Medical Sciences, Tehran, Iran.
Parvin MirzaghavamiMedical Physics Department, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Mohammadreza KhanmohammadiChemistry Department, Faculty of Science, Imam Khomeini International University, Qazvin, Iran.
Hamidreza SalighehRadQuantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Institute for Advanced Medical Imaging, Tehran University of Medical Sciences, Tehran, Iran. h-salighehrad@tums.ac.ir.ORCID http://orcid.org/0000-0001-7391-4135

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main aim of this study was to propose a new statistical method for evaluation of spatial malignancy distribution within Magnetic Resonance Spectroscopy (MRS) grid in Glioblastoma Multiforme patients. Voxels with different malignancy probabilities were presented as a novel MRS-based Malignancy Probability Map (MPM). For this purpose, a predictive probability-based clustering approach was developed, including the two following steps: (1) Gaussian Mixture Model, (2) Quadratic Discriminate Analysis coupled with Genetic Algorithm. Clustered probability values from two methods were then integrated to exploit the MPM. Results show that the suggested method is able to estimate the malignancy distribution with over 90% sensitivity and specificity. The proposed MRS-based MPM has an acceptable accuracy for providing useful complementary information about regional diffuse glioma malignancy, with the potential to lead to better detection of tumoral regions with high probability of malignancy. So, it also may encourage the use of additional information of this map as a tool for dose painting.

Indexed as

Diagnosis, Computer-AssistedMagnetic Resonance SpectroscopyAlgorithmsBrainBrain NeoplasmsCluster AnalysisGlioblastomaHumansMagnetic Resonance ImagingNeoplasm GradingProbabilitySensitivity and SpecificityGaussian mixture modelGenetic algorithmGlioblastoma multiformMagnetic resonance spectroscopy

Identifiers

PMID29542059

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.