ArticleFrontiers in oncology2021
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 51 citations in OpenAlex.
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- Deep learning in breast cancer histopathology: predicting and detecting axillary lymph node metastasis.Virchows Archiv : an international journal of pathology · 2026Review
- A combined radiomics-clinical nomogram for assessing MRI-defined femoral Head collapse status in osteonecrosis of the femoral Head.Journal of applied clinical medical physics · 2026Article
- SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types.Nature cancer · 2026Article
- nnMIL: a generalizable multiple instance learning framework for computational pathology.Nature biomedical engineering · 2026Article
- Learnable frozen feature augmentation for few-shot biomarker prediction from pathology whole-slide images.Bioinformatics (Oxford, England) · 2026Article
- A novel model for assessing lymph node metastasis and the immune microenvironment in breast cancer by integrating digital pathology images and transcriptomics.Discover oncology · 2026Article
- 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
- Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings.Journal of pathology informatics · 2026Article
- Diversity over scale: Whole-slide image variety enables H&E foundation model training with fewer patches.Journal of pathology informatics · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology.JHEP reports : innovation in hepatology · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Article
- A multimodal knowledge-enhanced whole-slide pathology foundation model.Nature communications · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Article
- Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images.NPJ precision oncology · 2025Article
- Article
- Construction of a prediction model for axillary lymph node metastasis in breast cancer patients based on a multimodal fusion strategy of ultrasound and pathological images.Frontiers in oncology · 2025Article
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Authors and funding
10 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN.
methodsA total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model.
resultsThe best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density (
conclusionOur study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.
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