Predicting Efficacy of Neoadjuvant Immunotherapy in Lung Cancer based on Tertiary Lymphoid Structure and...

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.