Evidence map›Paper›PMID 29025721›Full record

ArticleAJNR. American journal of neuroradiology2017

Predictive Models in Differentiating Vertebral Lesions Using Multiparametric MRI.

R Rathore, A Parihar, D K Dwivedi, A K Dwivedi, N Kohli, R K Garg, A Chandra

Abstract read
In one paragraph

Article in AJNR. American journal of neuroradiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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

7 authors.

R RathoreFrom the Departments of Radiodiagnosis (R.R., A.P., D.K.D., N.K.).ORCID http://orcid.org/0000-0001-6775-2854
A PariharFrom the Departments of Radiodiagnosis (R.R., A.P., D.K.D., N.K.) anitparihar@gmail.com.ORCID http://orcid.org/0000-0002-8838-8729
D K DwivediFrom the Departments of Radiodiagnosis (R.R., A.P., D.K.D., N.K.).ORCID http://orcid.org/0000-0001-5472-976X
A K DwivediDivision of Biostatistics & Epidemiology (A.K.D.), Department of Biomedical Sciences, Texas Tech University Health Sciences Center, El Paso, Texas.ORCID http://orcid.org/0000-0003-4574-1761
N KohliFrom the Departments of Radiodiagnosis (R.R., A.P., D.K.D., N.K.).ORCID http://orcid.org/0000-0003-2233-9366
A ChandraNeurosurgery (A.C.), King George's Medical University, Lucknow, Uttar Pradesh, India.ORCID http://orcid.org/0000-0001-6327-7988

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeConventional MR imaging has high sensitivity but limited specificity in differentiating various vertebral lesions. We aimed to assess the ability of multiparametric MR imaging in differentiating spinal vertebral lesions and to develop statistical models for predicting the probability of malignant vertebral lesions. MATERIALS AND

methodsOne hundred twenty-six consecutive patients underwent multiparametric MRI (conventional MR imaging, diffusion-weighted MR imaging, and in-phase/opposed-phase imaging) for vertebral lesions. Vertebral lesions were divided into 3 subgroups: infectious, noninfectious benign, and malignant. The cutoffs for apparent diffusion coefficient (expressed as 10

resultsOf the lesions of the 126 patients, 62 were infectious, 22 were noninfectious benign, and 42 were malignant. The mean ADC was 1.23 ± 0.16 for infectious, 1.41 ± 0.31 for noninfectious benign, and 1.01 ± 0.22 mm

conclusionsOur study shows that multiparametric MRI differentiates various vertebral lesions, and we established prediction models for the same.

Indexed as

AdolescentAdultAgedChildChild, PreschoolDiagnosis, DifferentialDiffusion Magnetic Resonance ImagingFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedModels, StatisticalSensitivity and SpecificitySpinal DiseasesSpinal Neoplasms

Identifiers

PMID29025721
PMCPMC7963730

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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.