Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images



Traditional 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.


921 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.


We 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.


The 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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This work was supported by the National Key Research and Development Program of China (2018YFC0910700), the Major Program of National Natural Science Foundation of China (91959205), and Capital’s Funds for Health Improvement and Research, CFH, No. 2018-2Z-1026.

Author information




Conception and design: YS and JJ; development and methodology: YH, FS, and JL; acquisition of data (acquired and managed patients, provided facilities, etc.): YH, KD, XW, and YJ; analysis and interpretation of data (e.g., statistical analysis, computational analysis): YH, FS, KD, XW, XZ, JL, JJ, and YS; writing, review, and/or revision of the manuscript: YH, FS, KD, XW, XZ, JL, JJ, and YS; administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): YS, JJ, and JL.

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Correspondence to Yu Sun.

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Hu, Y., Su, F., Dong, K. et al. Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images. Gastric Cancer (2021).

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  • Gastric cancer
  • Deep learning
  • Lymph node quantification
  • Lymph node metastasis