Evidence map›Paper›PMID 35757232›Full record

ArticleEuropean journal of radiology open2022

Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images.

Lorenzo Faggioni, Michela Gabelloni, Fabrizio De Vietro, Jessica Frey, Vincenzo Mendola, Diletta Cavallero, Rita Borgheresi, Lorenzo Tumminello, Jorge Shortrede, Riccardo Morganti and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of radiology open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. 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

14 authors.

Lorenzo FaggioniAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Michela GabelloniAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Fabrizio De VietroAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Jessica FreyAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Vincenzo MendolaAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Diletta CavalleroAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Rita BorgheresiAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Lorenzo TumminelloAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Jorge ShortredeAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Riccardo MorgantiDepartment of Clinical and Experimental Medicine, Section of Statistics, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Veronica SecciaOtolaryngology, Audiology, and Phoniatric Operative Unit, Department of Surgical, Medical, Molecular Pathology, and Critical Care Medicine, Azienda Ospedaliero Universitaria Pisana, University of Pisa, 56124 Pisa, Italy.
Francesca CoppolaDepartment of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, 40138, Bologna, Italy.
Dania CioniAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Emanuele NeriAcademic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Differentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRI-based radiomic features in discriminating PA from WT in the preoperative setting. Methods: We retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org). Results: The most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA. Conclusion: Radiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT.

Indexed as

ADC, apparent diffusion coefficientAUC, area under the curveFNAC, fine needle aspiration cytologyGLCM, gray level co-occurrence matrixGLDM, gray level dependence matrixGLRLM, gray level run length matrixGLSZM, gray level size zone matrixHead and neck cancerIBSI Image, Biomarker Standardization InitiativeMagnetic resonance imagingNGTDM, neighboring gray tone difference matrixPA, pleomorphic adenomaParotid neoplasmPcfsT1W, post-contrast fat-suppressed T1-weightedPleomorphic adenomaRadiomicsROC, receiver operating characteristicsWarthin tumorWT, Warthin tumor

Identifiers

PMID35757232
PMCPMC9214819

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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.