Evidence map›Paper›PMID 33737777›Full record

ArticleThe Indian journal of radiology & imaging

Characterization of pediatric head and neck masses with quantitative analysis of diffusion-weighted imaging and measurement of apparent diffusion coefficients.

Ali Baiomy, Ayman Nada, Ahmed Gabr, Ayda Youssef, Esmat Mahmoud, Iman Zaky

Open access · diamondAbstract read
In one paragraph

Article in The Indian journal of radiology & imaging. 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
0.1field-weighted citation impact, top 46% of its field
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, 2 citations in OpenAlex.

  1. Review
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 at 4 institutions in 2 countries.

Ali BaiomyTexas Tech University Health Sciences Center El Paso, TX, USA.
Ayman NadaDepartment of Radiology, University of Missouri, MO, USA.
Ahmed GabrDepartment of Diagnostic and Interventional Radiology, National Cancer Institute, Cairo University, Egypt.
Ayda YoussefDepartment of Diagnostic and Interventional Radiology, National Cancer Institute, Cairo University, Egypt.
Esmat MahmoudDepartment of Diagnostic and Interventional Radiology, National Cancer Institute, Cairo University, Egypt.
Iman ZakyDepartment of Radiology, Children's Cancer Hospital, Egypt.
Cairo University · EGChildren Cancer Hospital · EGTexas Tech University · USUniversity of Missouri · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeOur objective was to investigate the accuracy of quantitative diffusion-weighted imaging (DWI) to determine the histopathologic diagnosis of pediatric head and neck lesions. MATERIALS AND

methodsThis retrospective study included 100 pediatric patients recently diagnosed with head and neck tumors. All patients underwent preoperative conventional magnetic resonance imaging (MRI) and DWI. Each lesion was evaluated according to signal characteristics, enhancement pattern, and diffusivity. The average apparent diffusion coefficient (ADC) obtained from each tumor was compared to the histological diagnosis of benign, locally malignant, or malignant categories.

resultsOur retrospective study showed a significant negative correlation between average ADC and tumor histopathologic diagnosis (

conclusionDiffusion-weighted MRI study is an accurate, fast, noninvasive, and nonenhanced technique that can be used to characterize head and neck lesions. DWI helps to differentiate malignant from benign lesions based on calculated ADC values. Additionally, DWI is helpful to guide biopsy target sites and decrease the rate of unnecessary invasive procedures.

Indexed as

Apparent diffusion coefficientDWIhead and neck massespediatric

Identifiers

PMID33737777
PMCPMC7954155
OpenAlexW3119716964

What OpenQuestion holds

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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.