Abstract

The search for reliable prognostic markers in oral squamous cell carcinoma (OSCC) remains a critical need. Tumor-infiltrating lymphocytes (TILs), particularly T lymphocytes, play a pivotal role in the immune response against tumors and are strongly correlated with favorable prognoses. Computational pathology has proven highly effective for histopathological image analysis, automating tasks such as cell detection, classification, and segmentation.

In the present study, we developed a StarDist-based model to automatically detect T lymphocytes in hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) of OSCC, bypassing the need for traditional immunohistochemistry (IHC). Using QuPath, we generated training datasets from annotated slides, employing IHC as the ground truth. Our model was validated on Cancer Genome Atlas-derived OSCC images, and survival analyses demonstrated that higher TIL densities correlated with improved patient outcomes.

This work introduces an efficient, AI-powered workflow for automated immune profiling in OSCC, offering a reproducible and scalable approach for diagnostic and prognostic applications.

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Authors

Angela Crispino - Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples “Federico II”, Naples, Italy; These authors equally contributed to this work

Silvia Varricchio - Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples “Federico II”, Naples, Italy; These authors equally contributed to this work

Daniela Russo - Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples “Federico II”, Naples, Italy

Rosa Maria Di Crescenzo - Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples “Federico II”, Naples, Italy

Stefania Staibano - Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples “Federico II”, Naples, Italy; co-senior authors

Francesco Merolla - Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy; co-senior authors

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright

Copyright (c) 2024 Società Italiana di Anatomia Patologica e Citopatologia Diagnostica, Divisione Italiana della International Academy of Pathology

How to Cite
Crispino, A., Varricchio, S., Russo, D., Di Crescenzo, R. M., Staibano, S., & Merolla, F. . (2024). A Digital Workflow for Automated Assessment of Tumor-Infiltrating Lymphocytes in Oral Squamous Cell Carcinoma Using QuPath and a StarDist-Based Model. Pathologica - Journal of the Italian Society of Anatomic Pathology and Diagnostic Cytopathology, 116(6). Retrieved from https://www.pathologica.it/article/view/901
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