ReviewJournal of magnetic resonance imaging : JMRI2017
Diffusion-weighted breast MRI: Clinical applications and emerging techniques.
Review in Journal of magnetic resonance imaging : JMRI, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 173 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
173 citing papers in PubMed, 4 syntheses or guidelines pooled it, 331 citations in OpenAlex.
- A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions.Journal of cancer research and clinical oncology · 2024Pooled it
- Applications of TFrontiers in oncology · 2022Pooled it
- Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group.European radiology · 2020Guideline
- Breast lesions classified as probably benign (BI-RADS 3) on magnetic resonance imaging: a systematic review and meta-analysis.European radiology · 2018Pooled it
- Sodium accumulation in breast cancer predicts malignancy and treatment response.British journal of cancer · 2022Trial
- Additive value of diffusion-weighted MRI in the I-SPY 2 TRIAL.Journal of magnetic resonance imaging : JMRI · 2019Trial
- A multiparametric [European journal of nuclear medicine and molecular imaging · 2019Trial
- Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial.Radiology · 2018Trial
- Application of MR Cytometry in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparative Study.Journal of magnetic resonance imaging : JMRI · 2026Article
- Diffusion-weighted magnetic resonance imaging as a promising technique for detection and follow-up of liver metastases from neuroendocrine tumors: a retrospective and prospective study from the NETTARE task force.Journal of endocrinological investigation · 2026Observational
- Diagnostic value of a multiparametric magnetic resonance imaging-based nomogram incorporating apparent diffusion coefficient in preoperative differentiation of phyllodes tumors from breast cancer: a retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- Design of a nonlinear double-breast single-axis gradient coil with controlled anterior-posterior gradient variation for diffusion-weighted imaging.Magma (New York, N.Y.) · 2026Article
- Influence of co-registration on lesion characterization in diffusion-weighted breast MRI.Magma (New York, N.Y.) · 2026Article
- Can AI Detect What Is Not Injected? Evaluation of Lesion Detection in Virtual Contrast-Enhanced Breast MRI Using a Large-Scale AI Model Trained on GBCA-Enhanced Data.Tomography (Ann Arbor, Mich.) · 2026Article
- Automated Apparent Diffusion Coefficient Measurement of Breast Tumors in Diffusion-weighted MRI for Early Neoadjuvant Chemotherapy Response Assessment.Radiology. Imaging cancer · 2026Article
- Image Quality Assessment of Diffusion-Weighted Imaging (DWI) and Its Impact on Apparent Diffusion Coefficient (ADC) as a Quantitative Imaging Biomarker for Predicting Response to Neoadjuvant Chemotherapy in High-Risk Early Breast Cancer.Tomography (Ann Arbor, Mich.) · 2026Article
- Deep Learning-Based Synthetic Contrast-Enhanced Breast MRI for Monitoring Response to Neoadjuvant Therapy.Cancers · 2026Article
- The Future of Quantitative Breast Imaging: Integrated to Integral Imaging Biomarkers for Precision Cancer Care, From theAJR. American journal of roentgenology · 2026Review
- Hybrid [Bioengineering (Basel, Switzerland) · 2026Article
- Breast cancer pharmacotherapy and imaging interpretation: pathophysiological perspectives and clinical application.Japanese journal of radiology · 2026Review
113 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools. LEVEL OF EVIDENCE: 5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.
Indexed as
Identifiers
What OpenQuestion holds
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.