ArticleNature communications2024
AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- FGFR3 Alterations and Nectin-4 Expression as Therapeutic Biomarkers in Bladder Cancer: A Systematic Review and Single-Arm Meta-Analysis.International journal of molecular sciences · 2026Pooled it
- Paired Urinary Cytology and Targeted FGFR3 S249C/R248C Mutation Detection in Urine from Patients with Bladder Cancer: A Pilot Study.Journal of clinical medicine · 2026Article
- Toward comprehensive cellular characterization of H&E slides.Journal of pathology informatics · 2026Article
- Integrating liquid biopsy and mutational signatures to advance precision oncology.NPJ precision oncology · 2026Review
- Towards biomarker-driven therapies for urothelial carcinoma.Nature reviews. Clinical oncology · 2026Review
- Review
- Review
- SREBP2-RAB11A-ZDHHC20 axis orchestrates FGFR3 palmitoylation and membrane retention to drive bladder cancer progression.Cellular oncology (Dordrecht, Netherlands) · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Unraveling the FGFR-RNA splicing axis: Mechanisms, oncogenic crosstalks and innovations for therapeutic purpose.Acta pharmaceutica Sinica. B · 2026Review
- FGFR Aberrations in Solid Tumors: Mechanistic Insights and Clinical Translation of Targeted Therapies.Cancers · 2025Review
- Emerging technologies and clinical translation of urine-based liquid biopsy in urological cancers.Genes & genomics · 2025Review
- Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.RSC medicinal chemistry · 2025Review
- Comment on: "Artificial Intelligence Can Facilitate Application of Risk Stratification Algorithms to Bladder Cancer Patient Case Scenarios".Clinical Medicine Insights. Oncology · 2025Article
- AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer.Nature communications · 2024Article
- Computational pathology in bladder cancer: A scoping review.Bladder cancer (Amsterdam, Netherlands)Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathogenic activating mutations in the fibroblast growth factor receptor 3 (FGFR3) drive disease maintenance and progression in urothelial cancer. 10-15% of muscle-invasive and metastatic urothelial cancer (MIBC/mUC) are FGFR3-mutant. Selective targeting of FGFR3 hotspot mutations with tyrosine kinase inhibitors (e.g., erdafitinib) is approved for mUC and requires FGFR3 mutational testing. However, current testing assays (polymerase chain reaction or next-generation sequencing) necessitate high tissue quality, have long turnover time, and are expensive. To overcome these limitations, we develop a deep-learning model that detects FGFR3 mutations using routine hematoxylin-eosin slides. Encompassing 1222 cases, our study is a large-scale validation of a model prescreening FGFR3 mutations for MIBC and mUC patients. In this work, we demonstrate that our model achieves high sensitivity (>93%) on advanced and metastatic cases while reducing molecular testing by 40% on average, thereby offering a cost-effective and rapid pre-screening tool for identifying patients eligible for FGFR3 targeted therapies.
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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 Socratic graph.