Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis

作者：Zeyu Zhang, Yuanshen Zhao, Jingxian Duan, Yaou Liu, Hairong Zheng, Dong Liang, Zhenyu Zhang, Zhicheng Li · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2026 · DOI：10.1109/jbhi.2026.3721444 · 被引用次数：3 · 研究领域：AI in cancer detection

Accurate cancer survival prediction remains challenging due to tumor heterogeneity. While multi-modal data integration-particularly histopathology images and genomics-holds promise, effective fusion is hindered by biological complexity, gene detection variability, and limited cross-modal interpretability. To address this, we propose SurvPAGE, a Survival prediction model by fusing PAthological whole-slide images and RNA sequencing GEnomic data via biologically informed heterogeneous graph learning framework. SurvPAGE constructs modality-specific graphs encoding spatial histology patterns and functional genomic pathway interactions, connected by inter-modal edges modeling genotype-phenotype relationships. We introduce genGraphMAE, a masked graph autoencoder enhancing robustness against gene detection variability by reconstructing both pathway features and interactions from partially masked gene inputs. Attention-based graph learning dynamically fuses intra- and inter-modal contexts, while integrated gradients and attention heatmaps provide multi-scale interpretability, identifying prognostic histology regions and driver genes. Evaluated on lower-grade glioma, glioblastoma, and renal carcinoma datasets from TCGA and a local hospital, SurvPAGE achieves state-of-the-art performance in terms of C-index over existing multimodal benchmarks in survival prediction and reveals potential prognostic gene markers. This work improves cancer prognosis prediction by fusing pathology and genom...