SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.

Authors

Qiangjian Zhang, Na Zhao, Kai Song, Xun Ye, Hongxin Xiang, ZhiWei Zhao, Li Qi, Qing Zhang, Rui Guo, Junwei He, Jiaxin Liang, Shi Fu, Haifeng Wang, Xiaogang Li, Yingxia Wang, Yingying Pu, Jian Wang, Chunming Guo

Published in

International journal of surgery (London, England). Volume 112. Issue 7. Pages 13092-13104. Epub Jun 02, 2026.

Abstract

Bladder cancer survival outcomes exhibit significant heterogeneity, influenced by multifaceted factors. While digital pathology-based survival models leveraging artificial intelligence show promise, they often overlook complementary data sources. Conversely, imaging lacks cellular detail, and genomics/proteomics entail complexity and cost. To integrate multidimensional data for enhanced survival prediction, we propose SurvGRN, a multi-feature fusion framework. SurvGRN synergistically combines clinical variables, transcriptomics, and digital pathology slides using a gated residual network architecture. Pathological features are extracted via multiple instance learning, while clinical and transcriptomic data are processed as static inputs. These features are dynamically fused using a long short-term memory (LSTM) network for comprehensive survival risk assessment. Evaluated on 400 bladder cancer patients, SurvGRN significantly outperformed existing methods: improving the C-index by 12.6% over DeepMISL; 20.6% and 7.1% over graph-based models (DeepGraphConv and Patch-GCN); and 5.4% and 4.0% over attention-based approaches (Surformer and HVTSurv). Ablation studies confirmed the contributions of pathology features (extracted via ResNet-50 pre-trained on bladder tissue), clinical/transcriptomic data, and the LSTM fusion. SurvGRN also enabled significant stratification of patients into distinct risk cohorts. This work demonstrates that holistic integration of multi-source data through tailored fusion architectures substantially improves bladder cancer survival prediction.

PMID:
42682379
Bibliographic data and abstract were imported from PubMed on 02 Sep 2026.

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