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QuTILs: Open-Source Image-Based Infiltrating Immune Cell Detection for Research Application.

Mark Vater1, Roberto Salgado2, Elijah Blige1

  • 1The Ohio State University Wexner Medical Center.

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|May 25, 2026
PubMed
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This summary is machine-generated.

Stromal tumor infiltrating lymphocytes (sTILs) in triple-negative breast cancer (TNBC) predict better survival. The QuTILs computational method efficiently quantifies sTILs from digital H&E slides for research.

Area of Science:

  • Computational pathology
  • Digital pathology
  • Breast cancer research

Background:

  • Stromal tumor infiltrating lymphocytes (sTILs) are crucial prognostic and predictive biomarkers in triple-negative breast cancer (TNBC).
  • Accurate quantification of sTILs from hematoxylin and eosin (H&E) slides is essential for clinical research.
  • Digitalization of pathology slides enables computational analysis for sTIL enumeration.

Purpose of the Study:

  • To introduce QuTILs, an open-source, research-based computational approach for enumerating sTILs.
  • To validate the QuTILs method in large TNBC clinical trials.
  • To assess the association of QuTILs-derived sTIL percentage with patient outcomes.

Main Methods:

  • Development of a multilayer perceptron-based framework using QuPath software for sTIL identification.
Keywords:
Breast cancerdigital pathologytumor infiltrating lymphocytes

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  • Training and execution of the QuTILs algorithm on open-source H&E images from TNBC patients.
  • Application of QuTILs to H&E slides from two Phase III TNBC clinical trials (CALGB 40502 and 40603).

    • Main Results:

    • QuTILs demonstrated a significant univariate association with improved outcomes in the CALGB 40502 trial.
    • Higher sTIL percentages quantified by QuTILs correlated with reduced hazard (HR: 0.75, 95% CI: 0.63-0.91).
    • The prognostic significance of QuTILs was confirmed in multivariable models and validated in the CALGB 40603 trial.

    Conclusions:

    • QuTILs offers a computationally efficient and open-source workflow for sTIL identification in digital H&E images.
    • This method facilitates reproducible sTIL quantification for research applications in TNBC.
    • QuTILs can aid in understanding the role of sTILs in treatment response and survival in TNBC.