Survival prediction is a crucial task in the medical field and is essential for optimizing treatment options and resource allocation. However, current methods often rely on limited data modalities, resulting in suboptimal performance. In this paper, we propose an Integrated Cross-modal Fusion Network (ICFNet) that integrates histopathology whole slide images, genomic expression profiles, patient demographics, and treatment protocols. Specifically, three types of encoders, a residual orthogonal decomposition module and a unification fusion module are employed to merge multi-modal features to enhance prediction accuracy. Additionally, a balanced negative log-likelihood loss function is designed to ensure fair training across different patients. Extensive experiments demonstrate that our ICFNet outperforms state-of-the-art algorithms on five public TCGA datasets, including BLCA, BRCA, GBMLGG, LUAD, and UCEC, and shows its potential to support clinical decision-making and advance precision medicine. The codes are available at: https://github.com/binging512/ICFNet.

该研究解决了现有生存预测方法因依赖有限数据模态而导致的性能不足问题。提出的ICFNet通过融合组织病理全切片图像、基因表达谱、患者人口统计信息和治疗方案，利用多种编码器及模块增强预测准确性。实验表明，ICFNet在多个公共数据集上优于现有算法，对临床决策和精准医疗具有重要潜力。

ICFNet：用于生存预测的综合跨模态融合网络