ArticleEuropean radiology2024
Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer.
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 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deciphering the biological underpinnings behind prognostic MRI-based imaging signatures in breast cancer: a systematic review.Journal of translational medicine · 2025Pooled it
- Development and validation of a multimodal MRI habitat-based deep learning fusion model for predictingWorld journal of radiology · 2026Article
- Enhanced recurrence risk stratification for stages Ⅱ-Ⅲ breast cancer using MRI-based structure habitat imaging.European radiology · 2026Article
- Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk.BMC medical imaging · 2026Article
- 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
- A multimodal deep learning classifier for prediction of HER2-low expression in triple-negative breast cancer.BMC medical imaging · 2026Article
- Added value of intravoxel incoherent motion histogram analysis in differentiating HER2-zero, HER2-low, and HER2-positive breast cancer.La Radiologia medica · 2026Article
- Development and interpretation of a dual-energy CT-based deep learning radiomics model for predicting new cerebral ischemic lesions after carotid artery stenting: a multicenter study.European radiology · 2026Article
- Noninvasive stratification of HER2 status in breast cancer using multiparametric DECT: a prospective cohort study.International journal of surgery (London, England) · 2026Article
- Quantitative MRI mapping parameters to differentiate HER2-positive and HER2-low breast cancer.Quantitative imaging in medicine and surgery · 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
- Multiregional MRI-based deep learning radiomics to predict axillary response after neoadjuvant chemotherapy in breast cancer patients.Insights into imaging · 2026Article
- Machine Learning Model Based on Multiparametric MRI for Distinguishing HER2 Expression Level in Breast Cancer.Current oncology (Toronto, Ont.) · 2026Article
- Integrating intratumoral and peritumoral radiomics with deep transfer learning from multiparametric MRI for preoperative prediction of HER2 status in breast cancer: a multicenter study.Frontiers in oncology · 2026Article
- Multiparametric MRI and artificial intelligence for non-invasive HER2 assessment in breast cancer: a comprehensive review.Frontiers in medicine · 2026Review
- Intratumoral heterogeneity derived from synthetic MRI for characterization of HER2 subtypes in breast cancer.Frontiers in oncology · 2026Article
- HER2-Low Breast Cancer at the Interface of Pathology and Technology: Toward Precision Management.Biomedicines · 2025Review
- Automated machine learning-based unenhanced radiomics for identification of HER2-low breast cancers using diffusion-weighted imaging.BMC cancer · 2025Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Multimodal imaging and advanced quantitative techniques for HER-2 status prediction in breast cancer.Discover oncology · 2025Review
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14 authors.
Funding
Abstract
objectiveThis study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model.
methodsA total of 481 patients with breast cancer who underwent preoperative MRI were retrospectively recruited from two institutions. Traditional radiomics features and deep semantic segmentation feature-based radiomics (DSFR) features were extracted from segmented tumors to construct models separately. Then, the DLR model was constructed to assess the HER2 status by averaging the output probabilities of the two models. Finally, a Kaplan‒Meier survival analysis was conducted to explore the disease-free survival (DFS) in patients with HER2-low-positive status. The multivariate Cox proportional hazard model was constructed to further determine the factors associated with DFS.
resultsFirst, the DLR model distinguished between HER2-negative and HER2-overexpressing patients with AUCs of 0.868 and 0.763 in the training and validation cohorts, respectively. Furthermore, the DLR model distinguished between HER2-low-positive and HER2-zero patients with AUCs of 0.855 and 0.750, respectively. Cox regression analysis showed that the prediction score obtained using the DLR model (HR, 0.175; p = 0.024) and lesion size (HR, 1.043; p = 0.009) were significant, independent predictors of DFS.
conclusionsWe successfully constructed a DLR model based on MRI to noninvasively evaluate the HER2 status and further revealed prospects for predicting the DFS of patients with HER2-low-positive status. CLINICAL RELEVANCE STATEMENT: The MRI-based DLR model could noninvasively identify HER2-low-positive status, which is considered a novel prognostic predictor and therapeutic target. KEY POINTS: • The DLR model effectively distinguished the HER2 status of breast cancer patients, especially the HER2-low-positive status. • The DLR model was better than the traditional radiomics model or DSFR model in distinguishing HER2 expression. • The prediction score obtained using the model and lesion size were significant independent predictors of DFS.
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