Prediction of neoadjuvant therapy response in breast cancer based on interpretable artificial intelligence

Abstract

Background: To develop an AI-based predictive model for neoadjuvant therapy (NAT) efficacy in breast cancer, we integrated multimodal data and analyzed tumor microenvironment (TME) features to provide interpretability.

Methods: We retrospectively analyzed H&E-stained whole-slide images (WSIs) from a multi-center cohort of breast cancer patients receiving NAT to develop an AI predictive model. The cohort was stratified into training, test, internal validation, and external validation sets. Feature extraction used UNI and classification employed a multiple instance learning (MIL) framework. Model performance was evaluated via ROC curve analysis (AUC, precision, specificity, recall). Molecular mechanisms underlying model predictions were explored using TCGA multimodal data, integrating differential gene expression profiling with pathway enrichment analysis (GO, KEGG). TME component correlations with model scores were also investigated.

Results: The AI model demonstrated robust discriminative capacity across three residual cancer burden (RCB)-based classification tasks in 826 patients from two centers, achieving peak performance in subtask 2 (NAT-sensitive: RCB 0-1 vs. NAT-resistant: RCB 2-3). For subtask 2, AUCs were 0.901 (training), 0.858 (test), 0.808 (internal validation), and 0.819 (external validation). Molecular analysis linked the model's predictive efficacy to tumor cell cycle processes. TME analysis revealed positive correlations between model scores and activated immune cells (M0/M1 macrophages, dendritic cells), and negative correlations with inhibitory cells (M2 macrophages, resting mast cells). Crucially, the model's predictive scores were closely related to tumor-infiltrating lymphocytes (TILs), with spatial colocalization observed between classification weights and TILs distribution. Significant differences in TILs levels occurred across model score strata, validating the model's biological plausibility in predicting NAT response mechanisms.

Conclusion: We developed an interpretable AI model that predicts response to neoadjuvant therapy in breast cancer using H&E slides. The model's predictions are biologically interpretable, correlating with TME dynamics and spatial TIL patterns, offering a novel strategy for personalizing NAT treatment strategies.

Keywords: breast cancer; evaluation of therapeutic effect; explainable artificial intelligence; neoadjuvant therapy; tumor microenvironment.