精确的生存风险分层对于膀胱癌（BCa）的个性化治疗至关重要。这项研究开发并验证了一个端到端深度学习系统，使用组织学切片来预测 BCa 患者的总生存 (OS) 风险。我们采用 BlaPaSeg 瓦片分类器生成组织概率热图和分割图，训练了两个预后网络 MacroVisionNet 和 UniVisionNet，并探索了六种潜在的 BCa 预后生物标志物。在所有队列中，BlaPaSeg 的 AUC 范围为 0.9906 至 0.9945，而 MacroVisionNet 的 C 指数为 0.655 至 0.834，UniVisionNet 的 C 指数为 0.661 至 0.853。协变量调整后，MacroVisionNet 中高危人群的风险比 (HR) 值为 1.97 至 5.06，UniVisionNet 中高危人群的风险比 (HR) 值为 2.13 至 4.01。高风险 Coloc（肿瘤共定位评分）和 IMTS（综合肌肉肿瘤评分）组的死亡风险较高，HR 值为 1.41 至 10.16。该系统改进了 BCa 生存预测并支持精细的患者管理。© 2024。作者。

Precise survival risk stratification is crucial for personalized therapy in bladder cancer (BCa). This study developed and validated an end-to-end deep learning system using histological slides to predict overall survival (OS) risk in BCa patients. We employed the BlaPaSeg tile classifier to generate tissue probability heatmaps and segmentation maps, trained two prognostic networks, MacroVisionNet and UniVisionNet, and explored six potential BCa prognostic biomarkers. Across all cohorts, the AUC for BlaPaSeg ranged from 0.9906 to 0.9945, while the C-index varied from 0.655 to 0.834 for MacroVisionNet and 0.661 to 0.853 for UniVisionNet. After covariate adjustment, the hazard ratio (HR) values for high-risk groups were 1.97 to 5.06 in MacroVisionNet and 2.13 to 4.01 in UniVisionNet. The high-risk Coloc (Tumor Co-localization score) and IMTS (Integrated Muscle Tumor Score) groups illustrated a higher death risk with HR values from 1.41 to 10.16. The system improves BCa survival prediction and supports refined patient management.© 2024. The Author(s).