Evidence mapPaperPMID 41948525Full record

ArticleBreast cancer (Dove Medical Press)2026

Discrimination of Triple-Negative Breast Cancer: A Robust Clinical Baseline versus Multimodal Magnetic Resonance Imaging Integrated Models with Assessment of Generalizability.

Xueyan Liao, Xiaomin Wu, Junqiong Zheng, Guoliang Lin, Dandan Lin

Abstract read
In one paragraph

Article in Breast cancer (Dove Medical Press), 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

5 authors.

Xueyan LiaoDepartment of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, 364000, People's Republic of China.
Xiaomin WuDepartment of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, 364000, People's Republic of China.
Junqiong ZhengDepartment of Medical Oncology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, 364000, People's Republic of China.
Guoliang LinDepartment of Surgical Oncology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, 364000, People's Republic of China.
Dandan LinDepartment of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, 364000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to compare the potential performance of multimodal MRI-based models in discriminating TNBC. Methods: Clinical and MRI data from 162 female breast cancer patients diagnosed at our hospital between March 2022 and June 2024 were retrospectively collected. Clinical-conventional MRI model, qMRI model, radiomics model, and three integrated models were constructed on the borderline SMOTE-processed training set. ROC and DCA analyses were used to evaluate the discrimination performance and clinical net benefit. The optimal model was selected by validating the stability of the integrated models on the test set. Results: The mean age of 162 patients was 50.549 ± 9.563 years. No significant differences were found between the training set (n = 113) and the test set (n = 49) (all Conclusion: The clinical-conventional MRI model demonstrated stable discriminatory performance. While complex models show potential, their ability to generalize was limited by sample size, highlighting the need for validation with larger datasets.

Indexed as

magnetic resonance imagingmodelradiologytriple-negative breast cancer

Identifiers

PMID41948525
PMCPMC13052252

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.