Evidence map›Paper›PMID 32002644›Full record

ArticleEuropean radiology2020

Combination of an ultrafast TWIST-VIBE Dixon sequence protocol and diffusion-weighted imaging into an accurate easily applicable classification tool for masses in breast MRI.

Sandra C Peter, Evelyn Wenkel, Elisabeth Weiland, Matthias Dietzel, Rolf Janka, Arndt Hartmann, Julius Emons, Michael Uder, Stephan Ellmann

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In one paragraph

Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 18% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Abbreviated and Ultrafast Dynamic Contrast-enhanced (DCE) MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
    Review
  4. Ultrafast Breast MRI: A Narrative Review.Journal of personalized medicine · 2025
    Review
  5. Article
  6. Article
  7. Value for combination of TZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023
    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

9 authors at 3 institutions in 1 country.

Sandra C PeterDepartment of Radiology, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054, Erlangen, Germany.
Evelyn WenkelDepartment of Radiology, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054, Erlangen, Germany.
Elisabeth WeilandSiemens Healthcare GmbH, Allee am Röthelheimpark 2, 91052, Erlangen, Germany.
Matthias DietzelDepartment of Radiology, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054, Erlangen, Germany.
Rolf JankaDepartment of Radiology, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054, Erlangen, Germany.
Arndt HartmannInstitute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany.
Julius EmonsDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, 91054, Erlangen, Germany.
Michael UderDepartment of Radiology, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054, Erlangen, Germany.
Stephan EllmannDepartment of Radiology, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054, Erlangen, Germany. stephan.ellmann@uk-erlangen.de.ORCID http://orcid.org/0000-0003-2737-5526
Friedrich-Alexander-Universität Erlangen-Nürnberg · DEComprehensive Cancer Center Erlangen · DESiemens Healthineers (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to develop a tool for the classification of masses in breast MRI, based on ultrafast TWIST-VIBE Dixon (TVD) dynamic sequences combined with DWI. TVD sequences allow to abbreviate breast MRI protocols, but provide kinetic information only on the contrast wash-in, and because of the lack of the wash-out kinetics, their diagnostic value might be hampered. A special focus of this study was thus to maintain high diagnostic accuracy in lesion classification. MATERIALS AND

methodsSixty-one patients who received breast MRI between 02/2014 and 04/2015 were included, with 83 reported lesions (60 malignant). Our institute's standard breast MRI protocol was complemented by an ultrafast TVD sequence. ADC and peak enhancement of the TVD sequences were integrated into a generalised linear model (GLM) for malignancy prediction. For comparison, a second GLM was calculated using ADC and conventional DCE curve type. The resulting GLMs were evaluated for standard diagnostic parameters. For easy application of the GLMs, nomograms were created.

resultsThe GLM based on peak enhancement of the TVD and ADC was as equally accurate as the GLM based on conventional DCE and ADC, with no significant differences (sensitivity, 93.3%/93.3%; specificity, 91.3%/87.0%; PPV, 96.6%/94.9%; NPV, 84.0%/83.3%; all, p ≥ 0.315).

conclusionsThis study presents a method to integrate ultrafast TVD sequences into a breast MRI protocol, allowing a reduction of the examination time while maintaining diagnostic accuracy. A GLM based on the combination of TVD-derived peak enhancement and ADC provides high diagnostic accuracy, and can be easily applied using a nomogram. KEY POINTS: • Ultrafast TWIST-VIBE Dixon sequence protocols in combination with diffusion-weighted imaging allow to shorten breast MRI examinations, while diagnostic accuracy is maintained. • Integrating peak enhancement from the TWIST-VIBE Dixon sequence and the apparent diffusion coefficient into a generalised linear model provides a comprehensible image evaluation approach. • This approach is further facilitated by nomograms.

Indexed as

AlgorithmsAdultAgedBreast NeoplasmsDiffusion Magnetic Resonance ImagingFemaleHumansImage EnhancementMiddle AgedReproducibility of ResultsYoung AdultBreast cancerDiagnostic imagingDiffusion magnetic resonance imagingMagnetic resonance imagingStatistical model

Identifiers

PMID32002644
OpenAlexW3003898449

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.