ArticleEuropean radiology2024
Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed.
- Utilization of MRI-based radiomics nomogram for predicting HER2-zero, -low, and -overexpression breast cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- AI-based multimodal fusion for preoperative prediction of breast microcalcifications: combining mammography and tomosynthesis.BMC medical imaging · 2026Article
- The value of non-enhanced magnetic resonance imaging based on T2-weighted imaging and apparent diffusion coefficient in the study of three-tiered HER2 status in patients with breast cancer.Quantitative imaging in medicine and surgery · 2026Article
- Interpretable Whole-Breast Radiomic Biomarkers for Exploratory Assessment of HER2 + Breast Cancer in Digital Mammography.Journal of imaging informatics in medicine · 2026Article
- Development and validation of an MRI-based deep learning system for triple-class ER expression classification in breast cancer: a large-scale multicenter study.BMC medicine · 2026Article
- MRI quantification of intratumoral heterogeneity for predicting HER2-positive status in breast cancer: a retrospective multicenter study.BMC cancer · 2026Article
- Quantitative MRI mapping parameters to differentiate HER2-positive and HER2-low breast cancer.Quantitative imaging in medicine and surgery · 2026Article
- Value of intratumoral and peritumoral radiomics based on DCE-MRI in predicting HER2-low and HER2-zero in breast cancer.BMC medical imaging · 2026Article
- The result prediction of fluorescence in situ hybridization for breast cancer patients based on machine learning and deep learning models: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict HER2-negative breast cancer subtypes: a multicenter study.BMC medical imaging · 2026Article
- Stratified prediction of HER2 status in breast cancer by integrating intratumoral and peritumoral radiomics from DCE-MRI.BMC cancer · 2026Article
- A SHAP-interpretable XGBoost model: MRI-based intratumoral perfusion heterogeneity predicts HER2-zero, -low, and -positive ternary expression status in breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Machine Learning Model Based on Multiparametric MRI for Distinguishing HER2 Expression Level in Breast Cancer.Current oncology (Toronto, Ont.) · 2026Article
- Deep learning-based prediction of HER2 status from breast diffusion-weighted MRI.Frontiers in oncology · 2026Article
- Multiparametric MRI-based nomogram integrating clinicopathological factors for predicting HER2 expression status in breast cancer.Frontiers in oncology · 2026Article
- Development and validation of a multimodality radiomics-based nomogram for predicting HER2 expression status in invasive breast cancer.Frontiers in oncology · 2026Article
- Advancing breast cancer diagnosis: a combined approach using deep learning-reconstructed diffusion-weighted imaging and Synthetic MRI.Frontiers in oncology · 2026Article
- Multiparametric MRI-based radiomics of whole-tumor and habitat regions for predicting HER2 status in young breast cancer: a two-center study.Frontiers in oncology · 2026Article
- Integrative multi-omics and radiomics reveal a TMSB10-driven cell state for non-invasive assessment and precision stratification in breast cancer.Frontiers in immunology · 2026Article
- Multi-parametric MRI radiomics predicts different HER2 expression in breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
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12 authors.
Funding
Abstract
objectivesTo explore the performance of multiparametric MRI-based radiomics in discriminating different human epidermal growth factor receptor 2 (HER2) expressing statuses (i.e., HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer.
methodsA total of 771 breast cancer patients from two institutions were retrospectively studied. Five-hundred-eighty-one patients from Institution I were divided into a training dataset (n
resultsEleven radiomics features from DWI, ADC, and DCE; one radiomics feature from DWI; and 17 radiomics features from DWI, ADC, and DCE were selected to build three predictive models, respectively. In training, independent validation, and external validation datasets, radiomics models achieved AUCs of 0.809, 0.737, and 0.725 in differentiating HER2-overexpressing from others; 0.779, 0.778, and 0.782 in differentiating HER2-low-expressing from others; and 0.889, 0.867, and 0.813 in differentiating HER2-zero-expressing from others, respectively.
conclusionsMultiparametric MRI-based radiomics model may preoperatively predict HER2 statuses in breast cancer patients. CLINICAL RELEVANCE STATEMENT: The MRI-based radiomics models could be used to noninvasively identify the new three-classification of HER2 expressing status in breast cancer, which is helpful to the decision-making for HER2-target therapies. KEY POINTS: • Detecting HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing status in breast cancer patients is crucial for determining candidates for anti-HER2 therapy. • Radiomics features from multiparametric MRI significantly differed among HER2-overexpressing, HER2-low expressing, and HER2-zero-expressing breast cancers. • Multiparametric MRI-based radiomics could preoperatively evaluate three different HER2-expressing statuses and help to determine potential candidates for anti-HER2 therapy in breast cancer patients.
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