Evidence map›Paper›PMID 27449059›Full record

ArticleBreast cancer research : BCR2016

Breast MRI contrast enhancement kinetics of normal parenchyma correlate with presence of breast cancer.

Shandong Wu, Wendie A Berg, Margarita L Zuley, Brenda F Kurland, Rachel C Jankowitz, Robert Nishikawa, David Gur, Jules H Sumkin

Abstract read
In one paragraph

Article in Breast cancer research : BCR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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  14. Background parenchymal enhancement on breast MRI: A comprehensive review.Journal of magnetic resonance imaging : JMRI · 2020
    Review
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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

8 authors.

Shandong WuDepartment of Radiology, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA. wus3@upmc.edu.
Wendie A BergDepartment of Radiology, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
Margarita L ZuleyDepartment of Radiology, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
Brenda F KurlandUniversity of Pittsburgh Cancer Institute, Department of Biostatistics, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
Rachel C JankowitzMagee-Womens Hospital of University of Pittsburgh Medical Center, 300 Halket St, Pittsburgh, PA, 15213, USA.
Robert NishikawaDepartment of Radiology, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
David GurDepartment of Radiology, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
Jules H SumkinDepartment of Radiology, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.

Funding

Quantitative assessment of breast MRIs for breast cancer risk predictionR01CA193603 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI WU, SHANDONG · 2015 to 2018
$1.7M
NCI NIH HHS R01 CA193603
6 · The paper itself

Abstract

backgroundWe investigated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement kinetic variables quantified from normal breast parenchyma for association with presence of breast cancer, in a case-control study.

methodsUnder a Health Insurance Portability and Accountability Act compliant and Institutional Review Board-approved protocol, DCE-MRI scans of the contralateral breasts of 51 patients with cancer and 51 controls (matched by age and year of MRI) with biopsy-proven benign lesions were retrospectively analyzed. Applying fully automated computer algorithms on pre-contrast and multiple post-contrast MR sequences, two contrast enhancement kinetic variables, wash-in slope and signal enhancement ratio, were quantified from normal parenchyma of the contralateral breasts of both patients with cancer and controls. Conditional logistic regression was employed to assess association between these two measures and presence of breast cancer, with adjustment for other imaging factors including mammographic breast density and MRI background parenchymal enhancement (BPE). The area under the receiver operating characteristic curve (AUC) was used to assess the ability of the kinetic measures to distinguish patients with cancer from controls.

resultsWhen both kinetic measures were included in conditional logistic regression analysis, the odds ratio for breast cancer was 1.7 (95 % CI 1.1, 2.8; p = 0.017) for wash-in slope variance and 3.5 (95 % CI 1.2, 9.9; p = 0.019) for signal enhancement ratio volume, respectively. These odds ratios were similar on respective univariate analysis, and remained significant after adjustment for menopausal status, family history, and mammographic density. While percent BPE was associated with an odds ratio of 3.1 (95 % CI 1.2, 7.9; p = 0.018), in multivariable analysis of the three measures, percent BPE was non-significant (p = 0.897) and the two kinetics measures remained significant. For the differentiation of patients with cancer and controls, the unadjusted AUC was 0.71 using a combination of the two measures, which significantly (p = 0.005) outperformed either measure alone (AUC = 0.65 for wash-in slope variance and 0.63 for signal enhancement ratio volume).

conclusionsKinetic measures of wash-in slope and signal enhancement ratio quantified from normal parenchyma in DCE-MRI are jointly associated with presence of breast cancer, even after adjustment for mammographic density and BPE.

Indexed as

Contrast MediaImage EnhancementMagnetic Resonance ImagingAdultArea Under CurveBreast DensityBreast NeoplasmsCase-Control StudiesFemaleHumansImage Processing, Computer-AssistedMiddle AgedReproducibility of ResultsRetrospective StudiesRisk FactorsContrast MediaBreast cancerBreast MRIContrast enhancement kineticsNormal parenchymaQuantitative analysisSignal enhancement ratioWash-in slope

Identifiers

PMID27449059
PMCPMC4957890

What OpenQuestion holds

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LicenceCC BY
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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.