TPCL: A Tri-Modal Phase-Aware Contrastive Learning Framework for Multiphase CT, Clinical Data, and Medical Text Integration//

Hepatocellular carcinoma is a highly heterogeneous and complex malignant tumor, posing significant challenges for precise diagnosis and treatment. Existing methods face limitations in multimodal data integration and multi-phase feature modeling, making it difficult to fully exploit the complementary information from multi-phase CT images, structured clinical data, and medical texts. On one hand, most methods rely solely on single-modal data (e.g. CT images or clinical reports), failing to effectively utilize the complementary characteristics of multimodal data. On the other hand, traditional approaches typically concatenate multi-phase CT images as input to the model, ignoring the dynamic evolution features between different phases, leading to information loss and limited predictive performance. To address these issues, we propose a novel Tri-modal Phase-aware Contrastive Learning Framework (TPCL), which incorporates a Phase-aware Attention Fusion Network (PAAF-Net) and a Phase-Conditional Prompt Network (PCPN) to achieve deep alignment and integration of multimodal features. Additionally, we design a Multi-modal Contrastive Loss to further optimize the consistency of feature distributions across different modalities. Experimental results on multiple public and private datasets demonstrate that TPCL significantly outperforms existing methods, achieving up to an $18.54\%$ improvement in ACC and a $10.09\%$ improvement in AUC.