ArticleNPJ precision oncology2024
Integrated multicenter deep learning system for prognostic prediction in bladder cancer.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Development and validation of a multimodal AI-agent system for prognosis analysis of bladder urothelial carcinoma.NPJ precision oncology · 2026Article
- Survival prediction for bladder cancer using multimodal data with quantum neural networks and transformer architectures.Scientific reports · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Predicting efficacy in patients with locally advanced/metastatic urothelial carcinoma (mUC) treated with immunotherapy using explainable machine learning approaches: the SamUR-AI trial on behalf of the Meet-URO group.ESMO real world data and digital oncology · 2026Article
- Deep learning for prognostic stratification and biomarker exploration in upper tract urothelial carcinoma: a multicenter retrospective cohort study.International journal of surgery (London, England) · 2026Article
- Prior knowledge-guided multimodal deep learning system for biomarker exploration and prognosis prediction of urothelial carcinoma.NPJ digital medicine · 2025Article
- Risk Stratification Using a Perioperative Nomogram for Predicting the Mortality of Bladder Cancer Patients Undergoing Radical Cystectomy.Journal of clinical medicine · 2025Article
- Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images.Insights into imaging · 2025Article
- Accelerating and protective effects toward cancer growth in cGAS and FcgRIIb deficient mice, respectively, an impact of macrophage polarization.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Article
- Computational pathology in bladder cancer: A scoping review.Bladder cancer (Amsterdam, Netherlands)Review
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.