ArticleNPJ digital medicine2025
Predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer via interpretable multimodal deep learning.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02177695 (A Randomized Phase II Study of Co-Expression Extrapolation), which is not on this map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Randomized Phase II Study of Co-Expression Extrapolation (COXEN) With Neoadjuvant Chemotherapy for Localized, Muscle-Invasive Bladder Cancer
Who cites it
22 citing papers in PubMed.
- Development and validation of a computational histology artificial intelligence-powered prognostic biomarker in muscle-invasive bladder cancer.Urologic oncology · 2026Article
- Review
- Review
- Systems Biology and Multi-Omics Determinants of Response to Bladder-Preserving Trimodality Therapy in Muscle-Invasive Bladder Cancer.Life (Basel, Switzerland) · 2026Review
- Machine learning-based integration of transcriptome and digital pathology for predicting chemoresistance in muscle-invasive bladder cancer.Experimental & molecular medicine · 2026Article
- Comparative Molecular Insights and Computational Modeling of Multiple Myeloma and Osteosarcoma.International journal of molecular sciences · 2026Review
- MTHFD1L in muscle invasive bladder cancer: a multi-cohort study on prognosis and therapeutic response.World journal of surgical oncology · 2026Article
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Dynamic Precision Oncology for Real-Time Molecular Monitoring and Management in Urothelial Carcinoma.International journal of molecular sciences · 2026Review
- Review
- Development and validation of a cuproptosis-related lncRNA signature for predicting prognosis and immunotherapy response in breast cancer: a retrospective analysis of TCGA data.Translational cancer research · 2026Article
- Innovative In Vitro-In Silico Platform for Dose-Response Modeling in Canine Bladder Cancer: A 3D Organoid- and Mathematics-Based Approach.The AAPS journal · 2026Article
- UroFusion-X: a unified multimodal deep learning framework for robust diagnosis, subtyping, and prognosis of urological cancers.NPJ digital medicine · 2026Article
- Reprogramming the immunosuppressive breast cancer microenvironment: integrating cellular, metabolic, and stromal targets for rational immunotherapy.Frontiers in immunology · 2026Review
- Enhanced CT-based deep learning radiomics for bladder cancer prognosis.Military Medical Research · 2026Article
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Cytoskeletal protein KRT14 governs cisplatin resistance by modulating eIF4H-dependent ACOX2 translation and lipid metabolism in bladder cancer.Cell death & disease · 2025Article
- AI-informed computational pathology classifier predicts outcomes across treatment modalities in muscle-invasive urothelial carcinoma.Cancer letters · 2025Article
- Risk Stratification Using a Perioperative Nomogram for Predicting the Mortality of Bladder Cancer Patients Undergoing Radical Cystectomy.Journal of clinical medicine · 2025Article
- Common inflammatory proteins linking frailty and area-level deprivation as key drivers of cardiovascular risk in women.Communications medicine · 2025Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Building accurate prediction models and identifying predictive biomarkers for treatment response in Muscle-Invasive Bladder Cancer (MIBC) are essential for improving patient survival but remain challenging due to tumor heterogeneity, despite numerous related studies. To address this unmet need, we developed an interpretable Graph-based Multimodal Late Fusion (GMLF) deep learning framework. Integrating histopathology and cell type data from standard H&E images with gene expression profiles derived from RNA sequencing from the SWOG S1314-COXEN clinical trial (ClinicalTrials.gov NCT02177695 2014-06-25), GMLF uncovered new histopathological, cellular, and molecular determinants of response to neoadjuvant chemotherapy. Specifically, we identified key gene signatures that drive the predictive power of our model, including alterations in TP63, CCL5, and DCN. Our discovery can optimize treatment strategies for patients with MIBC, e.g., improving clinical outcomes, avoiding unnecessary treatment, and ultimately, bladder preservation. Additionally, our approach could be used to uncover predictors for other cancers.
Identifiers
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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.