Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study
Current clinical and molecular biomarkers lack reliable predictive power for gastrointestinal stromal tumor (GIST) patient response to neoadjuvant imatinib. To address this challenge, this study proposes an interpretable multimodal deep learning framework that integrates CT imaging with clinical variables through a cross-attention mechanism to enable synergistic cross-modal modeling. The approach leverages self-supervised pretraining, low-rank adaptation, and SMAC3-based hyperparameter optimization. The model achieves an AUC of 0.99 in internal validation, though external test performance ranges from 0.60 to 0.63. Interpretability analyses identify key discriminative features—including CD117, BRAF, PDGFRA, age, and sex—with FDR-corrected P-values ≤ 0.036, offering novel insights for personalized therapeutic decision-making.