UroFusion-X:一个统一的多式联络深度学习框架,用于对泌尿癌进行稳健的诊断,亚型和预后
Yingming Xiao1, Shengke Yang1, Mingjing He1
1Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041, Sichuan, China.
NPJ digital medicine
|January 19, 2026
概括
UroFusion-X集成了各种临床数据,用于泌尿癌诊断和预后. 这种多式模式框架提高了决策的一致性,并减少了临床实践中不必要的测试.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 多模式临床数据 (成像,病理学,奥米克,实验室) 在日常实践中是分散的.
- 这种碎片化导致尿道癌症管理中的决策不一致.
研究的目的:
- 提出UroFusion-X,一个统一的多式联运框架.
- 为了实现综合诊断,分子亚型和预后预测膀,和前列腺癌.
- 为了确保缺乏数据模式的稳定性.
主要方法:
- 集成的3D成像编码器,病理学多实例学习,奥米克图形网络和TabTransformer.
- 利用跨模式的共同注意力和专家产品融合以实现表示对齐.
- 采用了解剖病理一致性约束和对比学习,以实现可解释性和概括性.
- 集成的DeepSurv和DeepHit生存头部用于预后建模.
主要成果:
- 在多中心队列中,UroFusion-X的表现优于单模和简单融合基线.
- 保持了90%以上的预测性能,并大幅度放弃了模式.
- 与基线相比,在决策曲线分析中显示出更高的临床净益处.
结论:
- UroFusion-X框架提高了泌尿癌治疗的决策一致性.
- 它有可能减少临床工作流程中不必要的测试.
- 多模式数据集成对于强大的癌症诊断和预后至关重要.
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