ReviewFrontiers in radiology2023
Application of artificial intelligence in predicting lymph node metastasis in breast cancer.
Review in Frontiers in radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- An interpretable weighted ensemble based on routinely collected clinical data for the accurate prediction of axillary lymph node metastasis.Quantitative imaging in medicine and surgery · 2026Article
- AI-powered radiogenomics: imaging-driven diagnosis and discovery of cancer's molecular and cellular landscape.Molecular genetics and genomics : MGG · 2026Review
- Deep learning-powered multi-parametric ultrasound for classifying metastatic versus reactive axillary lymph nodes.Breast cancer research : BCR · 2025Article
- Axillary lymph node metastasis in breast cancer: from historical axillary surgery to updated advances in the preoperative diagnosis and axillary management.BMC surgery · 2025Review
- Concurrent presence of primary hemangioma and breast cancer metastasis within a lymph node: a case report inspired by the legacy of Professor Juan Rosai.Pathologica · 2024Article
- The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [Journal of imaging · 2023Article
- Automated Segmentation of Optical Coherence Tomography Images of the Human Tympanic Membrane Using Deep Learning.Algorithms · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.
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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.