Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline
Journal:
arXiv
Published Date:
Apr 23, 2025
Abstract
In this study, we built an end-to-end tumor-infiltrating lymphocytes (TILs)
assessment pipeline within QuPath, demonstrating the potential of easily
accessible tools to perform complex tasks in a fully automatic fashion. First,
we trained a pixel classifier to segment tumor, tumor-associated stroma, and
other tissue compartments in breast cancer H&E-stained whole-slide images (WSI)
to isolate tumor-associated stroma for subsequent analysis. Next, we applied a
pre-trained StarDist deep learning model in QuPath for cell detection and used
the extracted cell features to train a binary classifier distinguishing TILs
from other cells. To evaluate our TILs assessment pipeline, we calculated the
TIL density in each WSI and categorized them as low, medium, or high TIL
levels. Our pipeline was evaluated against pathologist-assigned TIL scores,
achieving a Cohen's kappa of 0.71 on the external test set, corroborating
previous research findings. These results confirm that existing software can
offer a practical solution for the assessment of TILs in H&E-stained WSIs of
breast cancer.
