Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline

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 classifie r 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 QuPa th 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 T IL 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.