Song Jinmiao, Hao Yatong, Zhao Shuang, Zhang Peng, Feng Qilin, Dai Qiguo, Duan Xiaodong
School of Software, Xinjiang University, Urumqi 830046, China.
School of Computer Science and Engineering, Dalian Minzu University, Dalian 116650, China.
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf103.
Survival prediction serves as a pivotal component in precision oncology, enabling the optimization of treatment strategies through mortality risk assessment. While the integration of histopathological images and genomic profiles offers enhanced potential for patient stratification, existing methodologies are constrained by two fundamental limitations: (i) insufficient attention to fine-grained local features in favor of global representations, and (ii) suboptimal cross-modal fusion strategies that either neglect intrinsic correlations or discard modality-specific information. To address these challenges, we propose DSCASurv, a novel cross-modal fusion alignment framework designed to explore and integrate intrinsic correlations across multimodal data, thereby improving the accuracy of survival prediction. Specifically, DSCASurv leverages the local feature extraction capabilities of convolutional layers and the long-range dependency modeling of scanning state space models to extract intra-modal representations, while generating cross-modal representations through dual parallel mixer architectures. A cross-modal attention module functions as a bridge for inter-modal information exchange and complementary information transfer. The framework ultimately integrates all intra-modal representations to generate survival predictions by enhancing and recalibrating complementary information. Extensive experiments on five benchmark cancer datasets demonstrate the superior performance of our approach compared to existing methods.
生存预测是精准肿瘤学的关键组成部分，通过死亡率风险评估能够优化治疗策略。虽然组织病理学图像和基因组图谱的整合为患者分层提供了更大的潜力，但现有方法受到两个基本限制：（i）过于关注全局表征而对细粒度局部特征关注不足，以及（ii）跨模态融合策略欠佳，要么忽略内在相关性，要么丢弃特定模态信息。为应对这些挑战，我们提出了DSCASurv，这是一种新颖的跨模态融合对齐框架，旨在探索和整合多模态数据中的内在相关性，从而提高生存预测的准确性。具体而言，DSCASurv利用卷积层的局部特征提取能力和扫描状态空间模型的长程依赖建模来提取模态内表征，同时通过双并行混合器架构生成跨模态表征。一个跨模态注意力模块充当模态间信息交换和互补信息传递的桥梁。该框架最终通过增强和重新校准互补信息来整合所有模态内表征以生成生存预测。在五个基准癌症数据集上进行的大量实验表明，我们的方法比现有方法具有更优的性能。
