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LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset.

Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni

    IEEE Journal of Biomedical and Health Informatics
    |October 25, 2023
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    Summary

    The Lymphocyte Assessment Hackathon (LYSTO) challenged participants to rapidly quantify T-cells in cancer images. Some methods achieved pathologist-level performance, establishing LYSTO as a valuable benchmark dataset.

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    Area of Science:

    • Computational pathology
    • Digital pathology
    • Medical image analysis

    Background:

    • Accurate quantification of tumor-infiltrating lymphocytes (TILs), particularly T-cells, is crucial for cancer diagnosis and prognosis.
    • Immunohistochemistry (IHC) staining for CD3 and CD8 markers is standard for visualizing T-cells in tissue samples.
    • Automating lymphocyte assessment can improve efficiency and consistency in histopathological analysis.

    Purpose of the Study:

    • To introduce the Lymphocyte Assessment Hackathon (LYSTO) as a rapid challenge for automated T-cell quantification in cancer.
    • To evaluate the performance of computational methods developed under time constraints for lymphocyte assessment.
    • To establish a benchmark dataset and evaluation platform for future research in automated digital pathology.

    Main Methods:

    • The LYSTO hackathon was organized in multiple phases alongside the MICCAI 2019 Conference.
    • Participants developed automated methods to count CD3+ and CD8+ T-cells in colon, breast, and prostate cancer IHC images within a few hours.
    • Methods were evaluated on-site, and subsequently on an independent lung cancer dataset, comparing performance against pathologists.

    Main Results:

    • Several participants developed methods capable of achieving pathologist-level performance in T-cell quantification.
    • The LYSTO challenge demonstrated the feasibility of rapid development for high-performance computational pathology tools.
    • Post-competition analysis confirmed the robustness of developed methods on unseen data.

    Conclusions:

    • Automated lymphocyte assessment methods can achieve expert performance, even when developed under strict time limitations.
    • The LYSTO hackathon successfully created a valuable, reusable benchmark dataset and evaluation framework for digital pathology.
    • This work highlights the potential of hackathon-style events to accelerate innovation in medical image analysis.