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Pathomics Image Analysis of Tumor Infiltrating Lymphocytes (TILs) in Colon Cancer.

Yuwei Zhang, Shahira Abousamra, Mahmudul Hasan

    Research Square
    |April 16, 2025
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    Deep learning Pathomics maps visualize tumor-infiltrating lymphocytes (TILs) in colon cancer. High TILs% predicts better survival, supporting its use in precision oncology.

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    Area of Science:

    • Computational Pathology
    • Oncology
    • Immunology

    Background:

    • Colon cancer exhibits complex interactions between tumor cells and immune cells.
    • Tumor-infiltrating lymphocytes (TILs) play a crucial role in anti-tumor immunity.
    • Quantifying TILs and their spatial distribution is vital for understanding tumor microenvironment.

    Purpose of the Study:

    • To develop a deep learning Pathomics workflow for spatial Tumor-TIL mapping in colon cancer.
    • To visualize and quantify the abundance and spatial distribution of TILs.
    • To evaluate the prognostic significance of TILs as a Pathomics biomarker.

    Main Methods:

    • Developed a deep learning Pathomics image analysis workflow.
    • Generated spatial Tumor-TIL maps from H&E stained whole slide images (WSIs).
    • Computed TILs% and performed survival analyses (Kaplan-Meier, Cox regression).

    Main Results:

    • Identified distinct TIL-rich and TIL-poor tumor habitats.
    • High TILs% was significantly associated with improved overall survival (OS).
    • High TILs% was also linked to improved progression-free interval (PFI) in colon cancer.

    Conclusions:

    • Pathomics Tumor-TIL mapping is a valuable tool for colon cancer research.
    • TILs% is a significant prognostic biomarker in localized and metastatic colon cancer.
    • Supports routine use in clinical trials, laboratory medicine, and precision oncology.