Evidence map›Paper›PMID 40103466›Full record

ArticleCurrent medical imaging2025

Background Parenchymal Enhancement in Breast MRI Correlates with Molecular Subtypes of Breast Cancer

Hongyu Liu, Xinyue Chen, Yanna Wang, Xiaoping Yang, Yuxingzi Chen

Abstract read
In one paragraph

Article in Current medical imaging, 2025. 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
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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.

  1. 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

5 authors.

Hongyu LiuDepartment of Radiology, Taizhou Central Hospital, Taizhou, China.
Xinyue ChenDepartment of Radiology, Taizhou Central Hospital, Taizhou, China.
Yanna WangDepartment of Radiology, Taizhou Central Hospital, Taizhou, China.
Xiaoping YangDepartment of Radiology, Taizhou Central Hospital, Taizhou, China.
Yuxingzi ChenDepartment of Nuclear Medicine, Taizhou Central Hospital, Taizhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeMRI could be considered as a non-destructive disease diagnosis procedure, this procedure does not allow directly molecular types of cancer. Herein, we aimed to evaluate the correlation of breast MRI background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) with the molecular subtypes and immunohistochemical markers of breast cancer.

methodsThis was a single-cross-sectional retrospective study.Fifty-six patients diagnosed with unilateral breast cancer who underwent breast MRI scans before needle biopsy or surgery were selected. The relationship between qualitative and quantitative BPE/FGT ratios and the expression of breast cancer molecular subtypes and immunohistochemical markers were evaluated in patients with breast cancer.

resultsQuantitative BPE (BPE%) of luminal A and luminal B was significantly lower than that of triple-negative breast cancer. There was no significant difference in the qualitative BPE/FGT between the different breast cancer subtypes. The quantitative BPE (BPE%) of estrogen receptor (ER)- negative tumors was higher than that of the ER-positive tumors, and the expression of FGT%, BPE%, and other immunohistochemical markers (human epidermal growth factor receptor-2(HER-2), progesterone receptor (PR), and Ki-67) were not significantly different. The proportion of high BPE distribution in HER-2 positive tumors was higher than that in the HER-2 negative group; however, there was no significant difference in the expression of qualitative BPE/FGT and other immunohistochemical markers (ER, PR, and Ki-67).

conclusionThere were significant differences in the levels of BPE among the different molecular subtypes. Therefore, BPE may be a potential imaging biomarker for the diagnosis of the molecular subtypes of breast cancer.

Indexed as

Breast NeoplasmsImage EnhancementMagnetic Resonance ImagingAdultAgedBiomarkers, TumorBreastCross-Sectional StudiesFemaleHumansMiddle AgedRetrospective StudiesBiomarkers, TumorBackground parenchymal enhancementBreast cancerDynamic enhancementFibroglandular Tissue (FGT).Magnetic resonance imagingMolecular subtype

Identifiers

PMID40103466
PMCPMC13096872

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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.