ArticleJournal of clinical medicine2020
Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it, 93 citations in OpenAlex.
- Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature.Technology in cancer research & treatmentPooled it
- Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters.Clinical and experimental medicine · 2023Trial
- Apparent diffusion coefficient and intravoxel incoherent motion-derived true diffusion serve as early non-contrast longitudinal biomarkers of neoadjuvant chemotherapy response.Scientific reports · 2026Article
- ITCNET: a visual and language pretraining model based on multimodal fusion in predicting the breast cancer subtypes on DCE-MRI.BMC medical informatics and decision making · 2026Article
- Imaging-Based Prediction of Key Breast Cancer Biomarkers Using Deep Learning on Digital Breast Tomosynthesis.European journal of breast health · 2026Article
- Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis.Diagnostics (Basel, Switzerland) · 2026Review
- Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis.Future oncology (London, England) · 2026Article
- Slice-level vs. volumetric radiomics for molecular subtyping of breast cancer: impact of patient-level data partitioning on model performance.Frontiers in radiology · 2026Article
- Multiparametric MRI-based radiomics nomogram for noninvasive stratification of HER2 expression status in breast cancer.Quantitative imaging in medicine and surgery · 2025Article
- Segmentation variability and radiomics stability for predicting triple-negative breast cancer subtype using magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Article
- Automatic Segmentation and Molecular Subtype Classification of Breast Cancer Using an MRI-based Deep Learning Framework.Radiology. Imaging cancer · 2025Article
- Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes.Scientific reports · 2025Article
- Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.Breast cancer research and treatment · 2025Article
- Article
- Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients.Breast cancer (Dove Medical Press) · 2025Article
- 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 · 2025Article
- Extracellular vesicles in triple-negative breast cancer: current updates, challenges and future prospects.Frontiers in molecular biosciences · 2025Review
- MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma.European journal of radiology open · 2024Article
- AI Applications to Breast MRI: Today and Tomorrow.Journal of magnetic resonance imaging : JMRI · 2024Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
Abstract
We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77-0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75-0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.
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Registered trials
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.