An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer
This provides an automated prognostic tool for breast cancer patients, though it is incremental as it builds on existing deep learning methods for medical imaging.
The study tackled automated scoring of tumour-infiltrating lymphocytes (TILs) in breast cancer whole-slide images using a deep learning pipeline based on Efficient-UNet, achieving state-of-the-art performance in segmentation and detection, with competitive results in predicting survival outcomes.
Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast cancer. In this study, we introduce an innovative deep learning pipeline based on the Efficient-UNet architecture to predict the TILs score for breast cancer whole-slide images (WSIs). We first segment tumour and stromal regions in order to compute a tumour bulk mask. We then detect TILs within the tumour-associated stroma, generating a TILs score by closely mirroring the pathologist's workflow. Our method exhibits state-of-the-art performance in segmenting tumour/stroma areas and TILs detection, as demonstrated by internal cross-validation on the TiGER Challenge training dataset and evaluation on the final leaderboards. Additionally, our TILs score proves competitive in predicting survival outcomes within the same challenge, underscoring the clinical relevance and potential of our automated TILs scoring pipeline as a breast cancer prognostic tool.