ArticleClinical and translational medicine2020
Automatic detection of squamous cell carcinoma metastasis in esophageal lymph nodes using semantic segmentation.
Article in Clinical and translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 23 citations in OpenAlex.
- Weakly supervised artificial intelligence for multi-cancer detection of lymph node metastasis on whole slide images.Nature communications · 2026Article
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Lymph Node Metastasis Pattern and Influencing Factors in Esophageal Squamous Cell Carcinoma.Aging medicine (Milton (N.S.W)) · 2026Article
- High-sensitivity pan-cancer AI assessment of lymph node metastasis via uncertainty quantification.NPJ digital medicine · 2026Article
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 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
- Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer.Cancers · 2024Article
- Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.BMJ open · 2023Article
- High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.BMC pulmonary medicine · 2023Article
- Review
- A systematic review on application of deep learning in digestive system image processing.The Visual computer · 2023Review
- Evaluation of a Deep Learning Approach to Differentiate Bowen's Disease and Seborrheic Keratosis.Cancers · 2022Article
- Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2021Article
- Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature.Frontiers in genetics · 2021Review
- Automatic detection of squamous cell carcinoma metastasis in esophageal lymph nodes using semantic segmentation.Clinical and translational medicine · 2020Article
- Knockdown of circRNA circ_0087378 Represses the Tumorigenesis and Progression of Esophageal Squamous Cell Carcinoma Through Modulating the miR-140-3p/E2F3 Axis.Frontiers in oncology · 2020Article
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Authors and funding
16 authors at 2 institutions in 2 countries.
Funding
Abstract
Esophageal squamous cell carcinoma (ESCC) is more prevalent than esophageal adenocarcinoma in Asia, especially in China, where more than half of ESCC cases occur worldwide. Many studies have reported that the automatic detection of lymph node metastasis using semantic segmentation shows good performance in breast cancer and other adenocarcinomas. However, the detection of squamous cell carcinoma metastasis in hematoxylin-eosin (H&E)-stained slides has never been reported. We collected a training set of 110 esophageal lymph node slides with metastasis and 132 lymph node slides without metastasis. An iPad-based annotation system was used to draw the contours of the cancer metastasis region. A DeepLab v3 model was trained to achieve the best fit with the training data. The learned model could estimate the probability of metastasis. To evaluate the effectiveness of the detection model of learned metastasis, we used another large cohort of clinical H&E-stained esophageal lymph node slides containing 795 esophageal lymph nodes from 154 esophageal cancer patients. The basic authenticity label for each slide was confirmed by experienced pathologists. After filtering isolated noise in the prediction, we obtained an accuracy of 94%. Furthermore, we applied the learned model to throat and lung lymph node squamous cell carcinoma metastases and achieved the following promising results: an accuracy of 96.7% in throat cancer and an accuracy of 90% in lung cancer. In this work, we organized an annotated dataset of H&E-stained esophageal lymph node and trained a deep neural network to detect lymph node metastasis in H&E-stained slides of squamous cell carcinoma automatically. Moreover, it is possible to use this model to detect lymph nodes metastasis in squamous cell carcinoma from other organs. This study directly demonstrates the potential for determining the localization of squamous cell carcinoma metastases in lymph node and assisting in pathological diagnosis.
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