ArticleFrontiers in oncology2021
Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes.
Article in Frontiers in oncology, 2021. 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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Diffusion-Weighted MRI for the Assessment of Molecular Prognostic Biomarkers in Breast Cancer.Korean journal of radiology · 2024Pooled it
- Article
- Multimodal imaging and advanced quantitative techniques for HER-2 status prediction in breast cancer.Discover oncology · 2025Review
- Modular breast and tumor perfusion phantoms for 4D dynamic contrast-enhanced dedicated breast CT.Medical physics · 2025Article
- Evaluating dynamic contrast-enhanced MRI for differentiating HER2-zero, HER2-low, and HER2-positive breast cancers in patients undergoing neoadjuvant chemotherapy.European journal of medical research · 2025Article
- Multiparametric MRI for characterization of the tumour microenvironment.Nature reviews. Clinical oncology · 2024Review
- The role of multiparametric MRI in predicting lymphovascular invasion in breast cancer patients.Future oncology (London, England) · 2024Article
- Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response.Insights into imaging · 2023Article
- Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI.BMC medical imaging · 2023Article
- Development and validation of a point-based scoring system for predicting axillary lymph node metastasis and disease outcome in breast cancer using clinicopathological and multiparametric MRI features.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Readout-Segmented Echo-Planar Diffusion-Weighted MR Imaging Improves the Differentiation of Breast Cancer Receptor Statuses Compared With Conventional Diffusion-Weighted Imaging.Journal of magnetic resonance imaging : JMRI · 2022Article
- Multimodality imaging in lobular breast cancer: Differences in mammography, ultrasound, and MRI in the assessment of local tumor extent and correlation with molecular characteristics.Frontiers in oncology · 2022Article
- Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging.BioMed research international · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess breast cancer receptor status and molecular subtypes by using the CAIPIRINHA-Dixon-TWIST-VIBE and readout-segmented echo-planar diffusion weighted imaging techniques.
methodsA total of 165 breast cancer patients were retrospectively recruited. Patient age, estrogen receptor, progesterone receptor, human epidermal growth factorreceptor-2 (HER-2) status, and the Ki-67 proliferation index were collected for analysis. Quantitative parameters (K
resultsYounger age (<49.5 years, odds ratio (OR) =0.95,
conclusionsK
Indexed as
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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.