ArticleNature communications2022
Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 63 citations in OpenAlex.
- Weakly supervised artificial intelligence for multi-cancer detection of lymph node metastasis on whole slide images.Nature communications · 2026Article
- Deep learning-based prediction of lymph node metastasis and occult tumor cells in gastric cancer using histopathological images: a retrospective study.British journal of cancer · 2026Article
- PRET is a few-shot system for pan-cancer recognition without example training.Nature cancer · 2026Article
- His-MMDM: Multi-Domain and Multi-Omics Translation of Histopathological Images with Diffusion Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Assessing the influence of two deep-learning assistance modes on pathologists in cancer identification.Diagnostic pathology · 2026Article
- [Artificial intelligence in diagnostics-a pathology perspective].Pathologie (Heidelberg, Germany) · 2026Review
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Computational Pathology with Topological signatures and Visual Word Encoding.Research square · 2025Article
- The role of artificial intelligence-based foundation models and "copilots" in cancer pathology: potential and challenges.Journal of experimental & clinical cancer research : CR · 2025Review
- Artificial intelligence-assisted enhancement of diagnostic accuracy and efficiency in detecting cervical lymph node metastases in oral squamous cell carcinoma.Journal of dental sciences · 2025Article
- HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data.Patterns (New York, N.Y.) · 2025Article
- Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning.Nature communications · 2025Article
- AI-assisted Diagnosis of Nonmelanoma Skin Cancer in Resource-Limited Settings.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
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- Advances in Breast Cancer Care: The Role of Artificial Intelligence and Digital Pathology in Precision Medicine.European journal of breast health · 2025Article
- AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions.Frontiers in public health · 2025Review
- Article
- Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review.Journal of pathology informatics · 2024Review
- The SCRUM-MONSTAR Cancer-Omics Ecosystem: Striving for a Quantum Leap in Precision Medicine.Cancer discovery · 2024Article
- An artificial intelligence model for detecting pathological lymph node metastasis in prostate cancer using whole slide images: a retrospective, multicentre, diagnostic study.EClinicalMedicine · 2024Article
Corrections and comments
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Authors and funding
18 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pathological identification of lymph node (LN) metastasis is demanding and tedious. Although convolutional neural networks (CNNs) possess considerable potential in improving the process, the ultrahigh-resolution of whole slide images hinders the development of a clinically applicable solution. We design an artificial-intelligence-assisted LN assessment workflow to facilitate the routine counting of metastatic LNs. Unlike previous patch-based approaches, our proposed method trains CNNs by using 5-gigapixel images, obviating the need for lesion-level annotations. Trained on 5907 LN images, our algorithm identifies metastatic LNs in gastric cancer with a slide-level area under the receiver operating characteristic curve (AUC) of 0.9936. Clinical experiments reveal that the workflow significantly improves the sensitivity of micrometastasis identification (81.94% to 95.83%, P < .001) and isolated tumor cells (67.95% to 96.15%, P < .001) in a significantly shorter review time (-31.5%, P < .001). Cross-site evaluation indicates that the algorithm is highly robust (AUC = 0.9829).
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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.