Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) breast cancer. In this study, we introduce an innovative deep learning pipeline based on the Efficient-UNet architecture to compute a TILs score for breast cancer whole slide images. Our pipeline first segments tumour-stroma regions and generates a tumour bulk mask. Subsequently, it detects 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 system as a breast cancer prognostic tool.

本研究引入一种创新的基于Efficient-UNet架构的深度学习流程，用于计算乳腺癌全切片图像中的肿瘤浸润淋巴细胞（TILs）得分。该方法在分割肿瘤/基质区域和检测TILs方面展示了最先进的性能，并在预测生存结果中证明了竞争力，显示了我们自动化TILs评分系统作为乳腺癌预后工具的临床相关性和潜力。

乳腺癌肿瘤浸润淋巴细胞评分的自动化流水线