Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning


Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist’s judgment, which relies heavily on subjective experience, or time-consuming molecular assays for subtype diagnosis. Here, we present a deep learning (DL) system to achieve interpretable tumor differentiation grade and microsatellite instability (MSI) recognition in gastric cancer directly using hematoxylin-eosin (HE) staining whole-slide images (WSIs). WSIs from 467 patients were divided into three cohorts: the training cohort with 348 annotated WSIs, the testing cohort with 88 annotated WSIs, and the integration testing cohort with 31 original WSIs without tumor contour annotation. First, the DL models comprehensibly achieved tumor differentiation recognition with an F1 values of 0.8615 and 0.8977 for poorly differentiated adenocarcinoma (PDA) and well-differentiated adenocarcinoma (WDA) classes. Its ability to extract pathological features about the glandular structure formation, which is the key to distinguishing between PDA and WDA, increased the interpretability of the DL models. Second, the DL models achieved MSI status recognition with a patient-level accuracy of 86.36% directly from HE-stained WSIs in the testing cohort. Finally, the integrated end-to-end system achieved patient-level MSI recognition from original HE staining WSIs with an accuracy of 83.87% in the integration testing cohort with no tumor contour annotation. The proposed system, therefore, demonstrated high accuracy and interpretability, which can potentially promote the implementation of artificial intelligence healthcare.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Construction of the DL system for tumor and MSI diagnosis.
Fig. 2: Development and validation of DL systems for tumor differentiation grade recognition.
Fig. 3: Development and validation of DL systems for MSI status recognition.
Fig. 4: Integration testing of the DL system for tumor and MSI diagnosis in unannotated WSIs.

Data availability

The dataset was collected and held by the Beijing Cancer Hospital. Annotated image tile datasets are available for noncommercial use at Other datasets analyzed during the current study are available from the corresponding author upon reasonable request.

Code availability

Source codes are available at


  1. Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019).

    CAS  Article  Google Scholar 

  2. Norgeot, B., Glicksberg, B. S. & Butte, A. J. A call for deep-learning healthcare. Nat. Med. 25, 14–15 (2019).

    CAS  Article  Google Scholar 

  3. Su, F. et al. Development and validation of a deep learning system for ascites cytopathology interpretation. Gastric Cancer 23, 1041–1050 (2020).

    Article  Google Scholar 

  4. Kather, J. N. et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med. 16, e1002730 (2019).

    Article  Google Scholar 

  5. Hu, Y. et al. Deep learning system for lymph nodes quantification and metastatic cancer identification from whole-slide pathology images. Gastric Cancer 24, 868–877 (2021).

    Article  Google Scholar 

  6. Kather, J. N. & Calderaro, J. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer. Nat. Rev. Gastroenterol. Hepatol. 17, 591–592 (2020).

    Article  Google Scholar 

  7. Arai, T. et al. Frequent microsatellite instability in papillary and solid-type, poorly differentiated adenocarcinomas of the stomach. Gastric Cancer 16, 505–512 (2013).

    CAS  Article  Google Scholar 

  8. Sugimura, H. Editorial: an obsession with subtyping gastric cancer. Gastric Cancer 16, 451–453 (2013). vol.

    Article  Google Scholar 

  9. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).

    Article  Google Scholar 

  10. Kanesaka, T. et al. Clinical predictors of histologic type of gastric cancer. Gastrointest. Endosc. 87, 1014–1022 (2017).

    Article  Google Scholar 

  11. Kuwata, T. et al. Establishment of novel gastric cancer patient-derived xenografts and cell lines: pathological comparison between primary tumor, patient-derived, and cell-line derived xenografts. Cells 8, 585 (2019).

    CAS  Article  Google Scholar 

  12. Feng, F. et al. Prognostic value of differentiation status in gastric cancer. BMC Cancer 18, 865 (2018).

    Article  Google Scholar 

  13. Liu, S. et al. Apparent diffusion coefficient value of gastric cancer by diffusion-weighted imaging: Correlations with the histological differentiation and Lauren classification. Eur. J. Radiol. 83, 2122–2128 (2014).

    Article  Google Scholar 

  14. Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054–1056 (2019).

    CAS  Article  Google Scholar 

  15. Cortes-Ciriano, I., Lee, S., Park, W.-Y., Kim, T.-M. & Park, P. J. A molecular portrait of microsatellite instability across multiple cancers. Nat. Commun. 8, 15180 (2017).

    CAS  Article  Google Scholar 

  16. Hause, R. J., Pritchard, C. C., Shendure, J. & Salipante, S. J. Classification and characterization of microsatellite instability across 18 cancer types. Nat. Med. 22, 1342–1350 (2016).

    CAS  Article  Google Scholar 

  17. Messersmith, W. A. NCCN guidelines updates: management of metastatic colorectal cancer. J. Natl. Compr. Cancer Netw. 17, 599–601 (2019). vol.

    Google Scholar 

  18. Li, K., Luo, H., Huang, L., Luo, H. & Zhu, X. Microsatellite instability: a review of what the oncologist should know. Cancer Cell Int. 20, 16 (2020).

    Article  Google Scholar 

  19. Hildebrand, L. A., Pierce, C. J., Dennis, M., Paracha, M. & Maoz, A. Artificial intelligence for histology-based detection of microsatellite instability and prediction of response to immunotherapy in colorectal cancer. Cancers. 13, 391 (2021).

    CAS  Article  Google Scholar 

  20. Yamashita, R. et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet. Oncol. 22, 132–141 (2021).

    Article  Google Scholar 

  21. Echle, A. et al. Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning. Gastroenterology 159, 1406–1416 (2020). e11.

    CAS  Article  Google Scholar 

  22. Pantanowitz, L. et al. Twenty years of digital pathology: an overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives. J. Pathol. Inform. 9, 40 (2018).

    Article  Google Scholar 

  23. Herrmann, M. D. et al. Implementing the DICOM standard for digital pathology. J. Pathol. Inform. 9, 37 (2018).

    Article  Google Scholar 

  24. Goode, A., Gilbert, B., Harkes, J., Jukic, D. & Satyanarayanan, M. OpenSlide: a vendor-neutral software foundation for digital pathology. J. Pathol. Inform. 4, 27 (2013).

    Article  Google Scholar 

  25. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article  Google Scholar 

  26. Macenko, M., et al. A method for normalizing histology slides for quantitative analysis. in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1107–1110 (2009).

  27. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition 770–778 (2016).

  28. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition 2921–2929 (2016).

  29. Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128, 336–359 (2020).

    Article  Google Scholar 

  30. Chen, Y.-C. et al. Clinicopathological variation of lauren classification in gastric cancer. Pathol. Oncol. Res. 22, 197–202 (2016).

    CAS  Article  Google Scholar 

  31. Baretti, M. & Le, D. T. DNA mismatch repair in cancer. Pharmacol. Ther. 189, 45–62 (2018).

    CAS  Article  Google Scholar 

  32. Fan, J. P., Qian, J. & Zhao, Y. J. The loss of PTEN expression and microsatellite stability (MSS) were predictors of unfavorable prognosis in gastric cancer (GC). Neoplasma 67, 1359–1366 (2020).

    CAS  Article  Google Scholar 

  33. Suraweera, N. et al. Evaluation of tumor microsatellite instability using five quasimonomorphic mononucleotide repeats and pentaplex PCR. Gastroenterology 123, 1804–1811 (2002).

    CAS  Article  Google Scholar 

  34. Hempelmann, J. A. et al. Microsatellite instability in prostate cancer by PCR or next-generation sequencing. J. Immunother. cancer 6, 29 (2018).

    Article  Google Scholar 

  35. Bilal, M., et al. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet. Digit. Heal. (2021)

  36. Muti, H. S. et al. Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. Lancet. Digit. Heal 3, e654–e664 (2021).

    Article  Google Scholar 

  37. Chen, Y. et al. The immune subtypes and landscape of gastric cancer and to predict based on the whole-slide images using deep learning. Front. Immunol. 12, 685992 (2021).

    CAS  Article  Google Scholar 

  38. Hinata, M. & Ushiku, T. Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning. Sci. Rep. 11, 22636 (2021).

    CAS  Article  Google Scholar 

  39. Ratti, M., Lampis, A., Hahne, J. C., Passalacqua, R. & Valeri, N. Microsatellite instability in gastric cancer: molecular bases, clinical perspectives, and new treatment approaches. Cell. Mol. Life Sci. 75, 4151–4162 (2018).

    CAS  Article  Google Scholar 

  40. Zhang, S. et al. REUR: a unified deep framework for signet ring cell detection in low-resolution pathological images. Comput. Biol. Med. 136, 104711 (2021).

    Article  Google Scholar 

Download references


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).

Author information

Authors and Affiliations



Conceptualization: Y.S., J.-F.J.; Methodology: F.S., J.L., X.Z., and B.W.; Formal analysis and investigation: F.S., J.L., X.Z., B.W., Y.H., and Y.S.; Writing, review, and/or revision of the manuscript: F.S., J.L., X.Z., B.W., Y.H., Y.S., and J.-F.J.; Funding acquisition: JL, YS; Resources: Y.S., J.-F.J.; Supervision: Y.S., J.L., J.-F.J.

Corresponding authors

Correspondence to Yu Sun or Jiafu Ji.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics Approval/Consent to Participate

The study was performed in accordance with relevant guidelines and regulations and approved by the institutional review board at Beijing Cancer Hospital. All patients provided written informed consent to participate in the institutional review board-approved protocol.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Su, F., Li, J., Zhao, X. et al. Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning. Lab Invest 102, 641–649 (2022).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


Quick links