ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2021
Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images.
Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 59 citations in OpenAlex.
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- AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis.Diagnostic pathology · 2025Article
- 2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.BMC medical imaging · 2025Article
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- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- 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
- An artificial intelligence-based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot-assisted rectal cancer surgery.Surgical endoscopy · 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
- Deep learning-driven diagnosis of multi-type vertebra diseases based on computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Deep learning-based pathology signature could reveal lymph node status and act as a novel prognostic marker across multiple cancer types.British journal of cancer · 2023Article
- Artificial Intelligence in the Pathology of Gastric Cancer.Journal of gastric cancer · 2023Review
- Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning.Medical & biological engineering & computing · 2023Article
- Review
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditional diagnosis methods for lymph node metastases are labor-intensive and time-consuming. As a result, diagnostic systems based on deep learning (DL) algorithms have become a hot topic. However, current research lacks testing with sufficient data to verify performance. The aim of this study was to develop and test a deep learning system capable of identifying lymph node metastases.
methods921 whole-slide images of lymph nodes were divided into two cohorts: training and testing. For lymph node quantification, we combined Faster RCNN and DeepLab as a cascade DL algorithm to detect regions of interest. For metastatic cancer identification, we fused Xception and DenseNet-121 models and extracted features. Prospective testing to verify the performance of the diagnostic system was performed using 327 unlabeled images. We further validated the proposed system using Positive Predictive Value (PPV) and Negative Predictive Value (NPV) criteria.
resultsWe developed a DL-based system capable of automated quantification and identification of metastatic lymph nodes. The accuracy of lymph node quantification was shown to be 97.13%. The PPV of the combined Xception and DenseNet-121 model was 93.53%, and the NPV was 97.99%. Our experimental results show that the differentiation level of metastatic cancer affects the recognition performance.
conclusionsThe diagnostic system we established reached a high level of efficiency and accuracy of lymph node diagnosis. This system could potentially be implemented into clinical workflow to assist pathologists in making a preliminary screening for lymph node metastases in gastric cancer patients.
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