Evidence map›Paper›PMID 38816553›Full record

ArticleInsights into imaging2024

Molecular subtypes of breast cancer identified by dynamically enhanced MRI radiomics: the delayed phase cannot be ignored.

Guoliang Huang, Siyao Du, Si Gao, Liangcun Guo, Ruimeng Zhao, Xiaoqian Bian, Lizhi Xie, Lina Zhang

Abstract read
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. 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
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  7. Review
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  15. MRI-Derived Radiomics For Classifying Breast Cancer Molecular Subtypes: a Modeling Approach.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2025
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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

8 authors.

Guoliang Huang *Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China.
Siyao Du *Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China.
Si GaoDepartment of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China.
Liangcun GuoDepartment of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China.
Ruimeng ZhaoDepartment of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China.
Xiaoqian BianDepartment of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China.
Lizhi XieGE Healthcare, Beijing, 100176, China.
Lina ZhangDepartment of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China. zhanglnda@163.com.ORCID http://orcid.org/0000-0002-4079-9692

Funding

National Scientific Foundation of China 81971695
6 · The paper itself

Abstract

objectivesTo compare the diagnostic performance of intratumoral and peritumoral features from different contrast phases of breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building radiomics models for differentiating molecular subtypes of breast cancer.

methodsThis retrospective study included 377 patients with pathologically confirmed breast cancer. Patients were divided into training set (n = 202), validation set (n = 87) and test set (n = 88). The intratumoral volume of interest (VOI) and peritumoral VOI were delineated on primary breast cancers at three different DCE-MRI contrast phases: early, peak, and delayed. Radiomics features were extracted from each phase. After feature standardization, the training set was filtered by variance analysis, correlation analysis, and least absolute shrinkage and selection (LASSO). Using the extracted features, a logistic regression model based on each tumor subtype (Luminal A, Luminal B, HER2-enriched, triple-negative) was established. Ten models based on intratumoral or/plus peritumoral features from three different phases were developed for each differentiation.

resultsRadiomics features extracted from delayed phase DCE-MRI demonstrated dominant diagnostic performance over features from other phases. However, the differences were not statistically significant. In the full fusion model for differentiating different molecular subtypes, the most frequently screened features were those from the delayed phase. According to the Shapley additive explanation (SHAP) method, the most important features were also identified from the delayed phase.

conclusionsThe intratumoral and peritumoral radiomics features from the delayed phase of DCE-MRI can provide additional information for preoperative molecular typing. The delayed phase of DCE-MRI cannot be ignored. CRITICAL RELEVANCE STATEMENT: Radiomics features extracted and radiomics models constructed from the delayed phase of DCE-MRI played a crucial role in molecular subtype classification, although no significant difference was observed in the test cohort. KEY POINTS: The molecular subtype of breast cancer provides a basis for setting treatment strategy and prognosis. The delayed-phase radiomics model outperformed that of early-/peak-phases, but no differently than other phases or combinations. Both intra- and peritumoral radiomics features offer valuable insights for molecular typing.

Indexed as

Breast neoplasmsMachine learningMagnetic resonance imaging

Identifiers

PMID38816553
PMCPMC11139827

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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.