Evidence map›Paper›PMID 42459307›Full record

ArticleFrontiers in oncology2026

Multimodal imaging features of primary breast epithelial-myoepithelial carcinoma: a case report and literature review.

Peilin Sha, Yunhao Luo, Xiangzhu Wang, Yanhong An, Jia Jia, Xue Shi, Xin Li, Mengke Xu, Huiyu Ge, Zexing Yu

Abstract readCase Reports
In one paragraph

Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Peilin Sha *The Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China.
Yunhao Luo *School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Xiangzhu WangThe Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China.
Yanhong AnDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Jia JiaBeijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Xue ShiBeijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Xin LiBeijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Mengke XuBeijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Huiyu GeBeijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Zexing YuBeijing Chaoyang Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast epithelial-myoepithelial carcinoma (EMC) is a rare biphasic malignant tumor composed of both epithelial and myoepithelial components, accounting for less than 0.5% of all breast tumors. Owing to its extremely low incidence, the imaging characteristics of EMC have not been fully described, particularly with regard to emerging ultrasound techniques, such as shear-wave elastography (SWE) and contrast-enhanced ultrasound (CEUS). Herein, we report the case of a 70-year-old woman with breast EMC. The multimodal imaging findings, including digital mammography, computed tomography (CT), magnetic resonance imaging (MRI), conventional ultrasound (grayscale and color Doppler), SWE, and CEUS, are systematically presented and analyzed in correlation with the pathological findings. This report aims to improve clinical understanding of the imaging features of EMC.

Indexed as

breast epithelial-myoepithelial carcinomacase reportcontrast-enhanced ultrasoundmultimodal imagingshear-wave elastography

Identifiers

PMID42459307
PMCPMC13368639

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.