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Colorectal cancer (CRC) patients are highly prone to liver metastasis (CRLM), which often becomes the leading cause of death in this population. Predicting the efficacy of first-line therapies is crucial for clinicians to develop personalized treatment strategies for CRLM patients. In this paper, we propose a novel multimodal cross-attention model that integrates contrast-enhanced liver CT imaging and clinical data to predict the therapeutic efficacy of first-line treatment in CRLM patients. Our approach utilizes the nnUNetv2 model to segment liver and intratumoral regions from CT scans. Radiomics features are extracted from the segmented tumor regions, followed by a feature selection process to identify key predictors of treatment efficacy. In parallel, highly correlated clinical variables are identified and preprocessed. The selected radiomic features and clinical variables are processed through two separate branches with identical structures, each incorporating a multi-head cross-attention module to enable efficient exchange and alignment of multimodal information. The fused multimodal features are subsequently used to predict therapeutic outcomes. Experiments conducted on a dataset of 177 patients demonstrate that our multimodal learning model outperforms uni-modal models and existing deep learning methods, achieving an AUC of 0.7195. This approach highlights the potential of integrating imaging and clinical data for improved treatment efficacy prediction in CRLM.Clinical relevance- This study highlights the importance of integrating contrast-enhanced liver CT imaging and clinical data for predicting the efficacy of first-line therapies in CRLM patients.