ArticleRadiology2023
Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer.
Article in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Detection of breast cancer lesions using APT weighted MRI: a systematic review.Journal of translational medicine · 2025Pooled it
- MMBCFNet: multi modal hybrid deep learning framework for breast cancer detection using MRI, mammography, and ultrasound images.Journal of ultrasound · 2026Article
- Deep learning for synthetic contrast-enhanced CT and MRI: a scoping review.European radiology · 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
- Deep learning-based generation of synthetic multiphasic MRI in hepatocellular carcinoma and cirrhosis.JHEP reports : innovation in hepatology · 2026Article
- Deep learning-based simulated contrast-enhanced MRI for rectal cancer evaluation: a multicenter study.Abdominal radiology (New York) · 2026Article
- An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.Nature communications · 2026Article
- Deep Learning-Based Synthetic Contrast-Enhanced Breast MRI for Monitoring Response to Neoadjuvant Therapy.Cancers · 2026Article
- Letter to the Editor: Synthesize contrast-enhanced ultrasound image of thyroid nodules via generative adversarial networks.European radiology · 2026Article
- Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI.Nature communications · 2026Article
- Establishing an Evidence-based Modern Breast MRI Program.Journal of breast imaging · 2026Review
- Synthesize contrast-enhanced ultrasound image of thyroid nodules via generative adversarial networks.European radiology · 2026Article
- Acquisition of KQuantitative imaging in medicine and surgery · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Virtual contrast-enhanced maximum intensity projections from high-b-value diffusion-weighted breast MRI: a feasibility study.European radiology experimental · 2025Article
- The Clinical Application of Artificial Intelligence in Breast Imaging: Current Insights, Challenges, and Future Directions.Journal of breast cancer · 2025Review
- Age-stratified deep learning model for thyroid tumor classification: a multicenter diagnostic study.European radiology · 2025Article
- Breast Diffusion-weighted MR Imaging: Current Applications, Insights from Screening, and Future Directions.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Applying a multi-task and multi-instance framework to predict axillary lymph node metastases in breast cancer.NPJ precision oncology · 2025Article
- Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks.European radiology experimental · 2025Article
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRI scans from precontrast MRI sequences in biopsy-proven invasive breast cancer with use of deep learning. Materials and Methods Women with invasive breast cancer and a contrast-enhanced breast MRI examination that was performed for initial evaluation of the extent of disease between January 2015 and December 2019 at a single academic institution were retrospectively identified. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRI scans from five precontrast sequences (T1-weighted non-fat-suppressed [FS], T1-weighted FS, T2-weighted FS, apparent diffusion coefficient, and diffusion-weighted imaging). For qualitative assessment, four breast radiologists (with 3-15 years of experience) blinded to whether the method of contrast was real or simulated assessed image quality (excellent, acceptable, good, poor, or unacceptable), presence of tumor enhancement, and maximum index mass size by using 22 pairs of real and simulated contrast-enhanced MRI scans. Quantitative comparison was performed using whole-breast similarity and error metrics and Dice coefficient analysis of enhancing tumor overlap. Results Ninety-six MRI examinations in 96 women (mean age, 52 years ± 12 [SD]) were evaluated. The readers assessed all simulated MRI scans as having the appearance of a real MRI scan with tumor enhancement. Index mass sizes on real and simulated MRI scans demonstrated good to excellent agreement (intraclass correlation coefficient, 0.73-0.86;
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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.