SynthesisCancer imaging : the official publication of the International Cancer Imaging Society2025
Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis.
Synthesis in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- Prediction model to prevent axillary surgery using axillary US and MRI in early breast cancer.European radiology · 2026Article
- An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.Nature communications · 2026Article
- Uncertainty-Aware Machine Learning for Predicting Axillary Lymph Node Metastasis Using Breast MRI.Breast cancer (Dove Medical Press) · 2026Article
- Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives.The Journal of international medical research · 2026Review
- Superparamagnetic iron oxide nanoparticles in clinical applications: current status and future perspectives.EBioMedicine · 2025Review
- Male Breast Cancer: Epidemiology, Diagnosis, Molecular Mechanisms, Therapeutics, and Future Prospective.Oncology research · 2025Review
- The Evolving Landscape of Deep Learning in Breast Cancer Imaging: A Bibliometric Study of Segmentation, Detection, and Diagnostic Research From 2006 to 2025.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo perform a systematic review and meta-analysis that assesses the diagnostic performance of deep learning algorithms applied to breast MRI for predicting axillary lymph nodes metastases in patients of breast cancer.
methodsA systematic literature search in PubMed, MEDLINE, and Embase databases for articles published from January 2004 to February 2025. Inclusion criteria were: patients with breast cancer; deep learning using MRI images was applied to predict axillary lymph nodes metastases; sufficient data were present; original research articles. Quality Assessment of Diagnostic Accuracy Studies-AI and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. A summary receiver operating characteristic curve (SROC) was performed. R statistical software (version 4.4.0) was used for statistical analyses.
resultsA total of 10 studies were included. The pooled sensitivity and specificity were 0.76 (95% CI, 0.67-0.83) and 0.81 (95% CI, 0.74-0.87), respectively, with both measures having moderate between-study heterogeneity (I
conclusionThis meta-analysis demonstrates that deep learning algorithms applied to breast MRI offer promising diagnostic performance for predicting axillary lymph node metastases in breast cancer patients. Incorporating deep learning into clinical practice may enhance decision-making by providing a non-invasive method to more accurately predict lymph node involvement, potentially reducing unnecessary surgeries.
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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.