DPVIT: A CIP-Assisted Diagnosis Model Based on Enhanced Swin Transformer Through Bitemporal CT Images

Checkpoint inhibitor pneumonitis (CIP) imposes substantial mortality risk in malignant tumor patients receiving immune checkpoint inhibitors (ICIs) therapy. Although computed tomography (CT) serves as the primary diagnostic modality for CIP, its heterogeneous imaging features pose critical challenges to clinical diagnosis. In this paper, we introduce DPVIT, a bitemporal CT analysis model designed to capture lung tissue changes. DPVIT features temporal phase embedding, encoding pre- and post-ICIs treatment scan timestamps for time-dependent pattern learning, and dynamic deformable convolution, which adapts sampling to detect subtle structural variations. Moreover, it replaces the Swin Transformer’s shifted window attention with a temporally extended grid attention module, enabling local-global feature interactions for multi-scale representation learning. In validation, DPVIT achieved an AUC score of 0.884, outperforming existing models and demonstrating its potential to improve the early and accurate diagnosis of CIP.