ArticleBMC cancer2024
Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Deep Learning for Detecting Axillary Lymph Node Metastasis in Breast Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Deep learning-based non-invasive prediction of axillary lymph node metastasis in breast cancer: performance of the YOLO-v11 object detection algorithm.BMC medical imaging · 2026Article
- A multimodal imaging-based decision tree for preoperative axillary risk stratification in breast cancer: a retrospective clinical utility analysis.American journal of cancer research · 2026Article
- Gradient perfusion features can effectively identify sentinel lymph node metastasis in early breast cancer.BMC cancer · 2025Article
- Deep learning-powered multi-parametric ultrasound for classifying metastatic versus reactive axillary lymph nodes.Breast cancer research : BCR · 2025Article
- Prediction of axillary lymph node metastasis in T1 breast cancer using diffuse optical tomography, strain elastography and molecular markers.Quantitative imaging in medicine and surgery · 2025Article
- Development and evaluation of a predictive model for postoperative recurrence and metastasis in breast cancer using an artificial intelligence ultrasound breast system.American journal of translational research · 2025Article
- Ultrasound-based radiomics combined with B3GALT4 level to predict sentinel lymph node metastasis in primary breast cancer.Frontiers in oncology · 2025Article
- Deep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application.Frontiers in physiology · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
purposeA practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study.
methodsA total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis.
resultsThe area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis.
conclusionThis model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.
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