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.

翻译：肿瘤浸润淋巴细胞（TILs）在三阴性乳腺癌及人表皮生长因子受体2（HER2）阳性乳腺癌中被视为有价值的预后标志物。本研究提出一种基于Efficient-UNet架构的创新深度学习流程，用于计算乳腺癌全切片图像的TILs评分。该流程首先分割肿瘤-间质区域并生成肿瘤整体掩膜，随后在肿瘤相关间质中检测TILs，通过精确模仿病理学家工作流程生成TILs评分。我们的方法在肿瘤/间质区域分割及TILs检测方面展现出最先进性能，这通过TiGER挑战赛训练数据集的内部交叉验证及最终排行榜评估得到验证。此外，本流程生成的TILs评分在同一挑战赛的生存结局预测中表现出竞争力，凸显了该自动化TILs评分系统作为乳腺癌预后工具的临床相关性与潜力。