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Neoadjuvant immunotherapy is an emerging treatment for lung cancer. However, its efficacy varies significantly due to the complexity of the immune microenvironment, and there is a lack of effective methods for predicting individualized efficacy of neoadjuvant immunotherapy. In this study, we propose a novel multi-instance learning model GLAM to mine fine-grained tertiary lymphoid structure (TLS) features and global immune microenvironment features from H&E-stained whole-slide images (WSI) to predict multiple clinical end-events that can reflect individualized short-term and long-term efficacy of neoadjuvant immunotherapy in lung cancer. We first train a network to predict TLS maturity, a prognostic indicator for immunotherapy, using a semi-supervised learning method. Then we combine fine-grained TLS features and global immune features via cross-attention and build a multi-instance learning model with self-attention to predict efficacy end-events. This study includes 194 lung cancer patients with post-operative WSI who received neoadjuvant immunotherapy, and the GLAM model demonstrates strong predictive performance across both short-term and long-term efficacy endpoints. For short-term efficacy endpoints, it achieves AUC=0.951 for predicting major pathological response, and AUC=0.864 for predicting pathological complete response. For long-term efficacy endpoints, it achieves AUC=0.911 for predicting 2.5-year recurrence status, and C-Index=0.805 for predicting individualized recurrence time.Clinical Relevance- This study provides a new method for predicting individualized short-term and long-term efficacy of neoadjuvant immunotherapy, which helps guiding personalized treatment planning for lung cancer patients undergoing neoadjuvant immunotherapy.