Evidence map›Paper›PMID 37017702›Full record

ArticleEuropean radiology2023

Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status.

Rosie Goodburn, Evanthia Kousi, Clarrissa Sanders, Alison Macdonald, Erica Scurr, Catey Bunce, Komel Khabra, Mamatha Reddy, Louise Wilkinson, Elizabeth O'Flynn and 2 more

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Quantitative MR Imaging of the Breast.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

Rosie GoodburnCRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK. rosie.goodburn@icr.ac.uk.ORCID http://orcid.org/0000-0001-5109-4738
Evanthia KousiCRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK.
Clarrissa SandersSt Georges University Hospitals NHS Foundation Trust, London, UK.
Alison MacdonaldThe Royal Marsden NHS Foundation Trust, Sutton, UK.
Erica ScurrThe Royal Marsden NHS Foundation Trust, Sutton, UK.
Catey BunceThe Royal Marsden NHS Foundation Trust, Sutton, UK.
Komel KhabraThe Royal Marsden NHS Foundation Trust, Sutton, UK.
Mamatha ReddySt Georges University Hospitals NHS Foundation Trust, London, UK.
Louise WilkinsonThe Royal Marsden NHS Foundation Trust, Sutton, UK.
Elizabeth O'FlynnThe Royal Marsden NHS Foundation Trust, Sutton, UK.
Steven AllenThe Royal Marsden NHS Foundation Trust, Sutton, UK.
Maria Angélica SchmidtCRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK.
Royal Marsden NHS Foundation Trust · GBSt George’s University Hospitals NHS Foundation Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer.

methodsPre-menopausal women aged 40-50 years old were scanned at 3 T, employing a standard breast protocol including a DCE-MRI (35 and 30 participants in high- and low-risk groups, respectively). The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts.

resultsIntra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk.

conclusionsThis study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement. KEY POINTS: • A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention. • Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection. • No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.

Indexed as

BreastBreast NeoplasmsAdultBreast DensityFemaleHumansMagnetic Resonance ImagingMiddle AgedReproducibility of ResultsRetrospective StudiesBiomarkersBreast densityFemaleParenchymal tissue, Magnetic resonance imaging

Identifiers

PMID37017702
PMCPMC10415521
OpenAlexW4362602134

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.