Evidence map›Paper›PMID 40289183›Full record

ArticleScientific reports2025

Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes.

Hao Xu, Ao Yang, Min Kang, Hua Lai, Xinzhu Zhou, Zhe Chen, Libo Lin, Peng Zhou, Heping Deng

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

  1. Article
  2. Integrating deep learning and radiomics for precise identification of luminal A/B breast cancer subtypes on dynamic contrast-enhanced MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Article
  3. Article
  4. Article
  5. 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

9 authors.

Hao Xu *Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Ao Yang *Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Min Kang *Department of Radiology, Sichuan Provincial Maternity and Child Health Care Hospital, Chengdu, China.
Hua Lai *Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Xinzhu Zhou *Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Zhe ChenDepartment of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Libo LinDepartment of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Peng ZhouDepartment of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Heping DengDepartment of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China. dengheping1@126.com.

Funding

National Natural Science Foundation of China U21A20521
6 · The paper itself

Abstract

Distinguishing the luminal subtypes of breast cancer (BC) remaining challenging. Thus, the aim of this study was to investigate the feasibility of radiomic signature using intratumoral and peritumoral features obtained from dynamic contrast-enhanced MRI (DCE-MRI) in preoperatively discriminating the luminal from non-luminal type in patients with BC. A total of 305 patients with pathologically confirmed BC from three hospitals were retrospectively enrolled. The LASSO method was then used for selecting features, and the radiomic score (radscore) for each patient was calculated. Based on the radscore, Radiomic signature of intratumoral, peritumoral, and combined intratumoral and peritumoral were established, respectively. The performances of the radiomic signatures were validated with receiver operator characteristic (ROC) curve and decision curve analysis. For predicting molecular subtypes, the AUC for intratumoral radiomic signature was 0.817, 0.838, and 0.883 in the training set, internal validation set, and external validation set, respectively. AUC for the peritumoral radiomic signature was 0.863, 0.895, and 0.889 in the training set, internal validation set, and external validation set, respectively. The AUC for combined intratumoral and peritumoral radiomic signature was 0.956, 0.945, and 0.896 in the training set, internal validation set, and external validation set, respectively. Additional contributing value of combined intratumoral and peritumoral radiomic signature to the intratumoral radiomic signature was statistically significant [NRI, 0.300 (95% CI: 0.117-0.482), P = 0.001 in internal validation set; NRI, 0.224 (95% CI: 0.038-0.410), P = 0.018 in external validation set]. These results indicated that the radiomic signature combining intratumoral and peritumoral features showed good performance in predicting the luminal type of breast cancer.

Indexed as

Breast NeoplasmsMagnetic Resonance ImagingAdultAgedContrast MediaDynamic Contrast Enhanced Magnetic Resonance ImagingFemaleHumansMiddle AgedRadiomicsRetrospective StudiesROC CurveContrast MediaBreast cancerMagnetic resonance imagingRadiomics

Identifiers

PMID40289183
PMCPMC12034752

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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.