Evidence map›Paper›PMID 41737186›Full record

ArticlePakistan journal of medical sciences2026

Value of Radiomics Based on DCE-MRI in distinguishing benign and malignant breast lesions: Predicting Histological Grade and Lymph Node Metastasis of Breast Cancer.

Peiru Li, Hui Xu

Abstract read
In one paragraph

Article in Pakistan journal of medical sciences, 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

2 authors.

Peiru LiPeiru Li Department of Radiology, Yongkang First People's Hospital, Yongkang, Zhejiang Province 321300, P.R. China.
Hui XuHui Xu Department of Radiology, Yongkang Hospital of Traditional Chinese Medicine, Yongkang, Zhejiang Province 321300, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The characteristics of benign and malignant breast lesions often overlap and intersect, leading to missed diagnosis or inaccurate diagnosis and excessive biopsy, besides surgical procedures. This study aimed to assess the diagnostic value of radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in evaluating the pathological characteristics of breast cancer. Methodology: This retrospective case-control study included 110 patients with breast lesions who underwent DCE-MRI and obtained pathological results at Yongkang First People's Hospital and Yongkang Hospital of Traditional Chinese Medicine from September 2019 to December 2024. According to the results, 55 patients with confirmed breast cancer lesions (malignant group) were matched with a cohort of patients with benign lesions (benign group) at a 1:1 ratio. Radiomic parameters from DCE-MRI were analyzed in the two groups. Results: The radiomic features derived from DCE-MRI showed significant differences not only between benign and malignant breast lesions but also among subgroups stratified by histological grade and axillary lymph node metastasis. Specifically, patients with higher histological grades demonstrated elevated values in long run emphasis and all-angle cluster prominence variance (×10 Conclusion: DCE-MRI-based radiomics can effectively differentiate benign and malignant breast lesions and has a predictive value for assessing histological grade and axillary lymph node status. Radiomic features may therefore serve as noninvasive imaging biomarkers to support breast cancer diagnosis, grading, and staging.

Indexed as

Breast cancerDCE-MRIDifferential diagnosisHistological gradeRadiomics

Identifiers

PMID41737186
PMCPMC12927111

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.