ArticleDiagnostics (Basel, Switzerland)2020
Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis.European radiology · 2025Pooled it
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
- Artificial Intelligence for Breast MRI Lesion Classification: A Targeted Evidence Synthesis and Meta-Analysis of Discriminative Performance and Heterogeneity.Diagnostics (Basel, Switzerland) · 2026Review
- Evaluation of Individual T1w-DIXON Contrasts for Subtraction Generation in Dynamic Contrast-Enhanced Breast MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Establishing an Evidence-based Modern Breast MRI Program.Journal of breast imaging · 2026Review
- Development of an Automated CAD System for Lesion Detection in DCE-MRI.Journal of imaging informatics in medicine · 2025Article
- AI-driven diffusion weighted imaging-based non-contrast protocol for breast cancer diagnosis: a multicentre, multidimensional validation study.EClinicalMedicine · 2025Article
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Predicting Very Early-Stage Breast Cancer in BI-RADS 3 Lesions of Large Population with Deep Learning.Journal of imaging · 2025Article
- Application of Machine Learning to Breast MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.Japanese journal of radiology · 2025Review
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- AI Applications to Breast MRI: Today and Tomorrow.Journal of magnetic resonance imaging : JMRI · 2024Review
- A Review of Artificial Intelligence in Breast Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review.European radiology · 2024Article
- Weakly Supervised Breast Lesion Detection in Dynamic Contrast-Enhanced MRI.Journal of digital imaging · 2023Article
- Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review.Breast cancer research : BCR · 2023Review
- The utilization of artificial intelligence applications to improve breast cancer detection and prognosis.Saudi medical journal · 2023Review
- Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care.AIMS public health · 2023Review
- Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (
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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.