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<p>Accurate survival prediction for urothelial carcinoma (UC) is critical but limited by a lack of AI systems that integrate multimodal data with prior knowledge. To address this gap, we developed a multimodal deep learning system that integrates histopathology, radiology, and structured pathology text. We incorporated prior knowledge to improve tumor segmentation and create knowledge-guided slide representations. CTContextNet and MacroContextNet were then employed for capturing radiological and macroscopic prognostic information. IM-NCTNet integrated multi-modality information for enhanced prediction. Across multi-center, large-scale, multi-cohort validation, the knowledge-guided prognostic system demonstrated higher performance over single-modality models, achieving C-index scores ranging from 0.809 to 0.867. The framework identified novel prognostic biomarkers related to infiltration patterns in muscle and renal parenchyma. High-risk Coloc_M, Coloc_R, and IMTS groups exhibited increased mortality risk, with hazard ratio values ranging from 2.47 to 16.38. The proposed AI framework offers a comprehensive and robust tool for UC prognosis, supporting refined patient management.</p>

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Prior knowledge-guided multimodal deep learning system for biomarker exploration and prognosis prediction of urothelial carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月26日

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  • Quanhao He,
  • Hao Tan,
  • Bangxin Xiao,
  • Yiwen Tan,
  • Xiang Peng,
  • Canjie Peng,
  • Xiaofeng Yue,
  • Linshan Jiang,
  • Youde Cao,
  • Fa Jin Lv,
  • Wenlong Zhao,
  • Hongmei Yi,
  • YingJia Liu,
  • Weiyang He,
  • Mingzhao Xiao

摘要

Accurate survival prediction for urothelial carcinoma (UC) is critical but limited by a lack of AI systems that integrate multimodal data with prior knowledge. To address this gap, we developed a multimodal deep learning system that integrates histopathology, radiology, and structured pathology text. We incorporated prior knowledge to improve tumor segmentation and create knowledge-guided slide representations. CTContextNet and MacroContextNet were then employed for capturing radiological and macroscopic prognostic information. IM-NCTNet integrated multi-modality information for enhanced prediction. Across multi-center, large-scale, multi-cohort validation, the knowledge-guided prognostic system demonstrated higher performance over single-modality models, achieving C-index scores ranging from 0.809 to 0.867. The framework identified novel prognostic biomarkers related to infiltration patterns in muscle and renal parenchyma. High-risk Coloc_M, Coloc_R, and IMTS groups exhibited increased mortality risk, with hazard ratio values ranging from 2.47 to 16.38. The proposed AI framework offers a comprehensive and robust tool for UC prognosis, supporting refined patient management.