Evidence map›Paper›PMID 41917870›Full record

ArticleBMC medical imaging2026

Value of intratumoral and peritumoral radiomics based on DCE-MRI in predicting HER2-low and HER2-zero in breast cancer.

Min Sun, Yuwei Wang, Ruoming Li, Weining Zhao, Haiqing Yang, Lixia Zhou, Duo Gao, Shuai Quan, Zuojun Geng

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

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Min SunDepartment of Magnetic Resonance Imaging, Cangzhou Central Hospital, Cangzhou, Hebei, China. sunmin987@163.com.
Yuwei WangXiongan Xuanwu Hospital, Xiongan, Hebei, China.
Ruoming LiDepartment of Magnetic Resonance Imaging, Hebei Medical University Affiliated Cangzhou Central Hospital, Cangzhou, Hebei, China.
Weining ZhaoDepartment of Magnetic Resonance Imaging, Hebei Medical University Affiliated Cangzhou Central Hospital, Cangzhou, Hebei, China.
Haiqing YangDepartment of Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Lixia ZhouDepartment of Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Duo GaoDepartment of Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Shuai QuanGE HealthCare China (Shanghai), Shanghai, 210000, China.
Zuojun GengDepartment of Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of this study was to develop and validate intratumoral and peritumoral radiomic models based on DCE-MRI to predict the human epidermal growth factor receptor 2 (HER2) low and zero states of breast cancer(BCa).

methodsThe clinical data of 168 patients with BCa were retrospectively analysed and divided into a training set and a validation set. The intratumoral and peritumoral radiomic features were extracted from DCE-MRI. Variance analysis, univariate logistic analysis, and least absolute shrinkage and selection operator (LASSO) were used as selection operators for dimension reduction. Feature selection and radiomics model construction were performed using max-relevance and min-redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) on the training cohort. Logistic regression (LR) was used as a classifier to construct the intratumoral, peritumoral and intratumoral combined peritumoral radiomic models of DCE-MRI. The optimal model was selected to construct the fusion model combined with the screened clinical independent risk factors, and the model was displayed as a nomogram. The performance of each model was evaluated via the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve.

resultsIn the end, a total of 12 features were retained, and the AUC values of the DCE-MRI intratumoral + peritumoral model in the training set and validation set were 0.845 and 0.836, respectively. Age was identified as an independent risk factor for predicting HER2 status in BCa through Univariate and multivariate analysis in the training set.The AUC values of the final constructed nomogram were 0.862 and 0.844 in the training set and validation set, respectively.

conclusionIntratumoral and peritumoral radiomic methods based on DCE-MRI have good value in the identification of HER2-low and HER-zero BCa. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Breast NeoplasmsErb-b2 Receptor Tyrosine KinasesAdultAgedContrast MediaDynamic Contrast Enhanced Magnetic Resonance ImagingFemaleHumansMagnetic Resonance ImagingMiddle AgedRadiomicsRetrospective StudiesContrast MediaERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesBreast cancerDynamic contrast-enhanced magnetic resonance imagingHuman epidermal growth factor receptor 2Magnetic resonance imagingPeritumourRadiomics

Identifiers

PMID41917870
PMCPMC13173861

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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.