Bannier, Pierre-Antoine and Saillard, Charlie and Mann, Philipp and Touzot, Maxime and Maussion, Charles and Matek, Christian and Kluemper, Niklas and Breyer, Johannes and Wirtz, Ralph and Sikic, Danijel and Schmitz-Draeger, Bernd and Wullich, Bernd and Hartmann, Arndt and Foersch, Sebastian and Eckstein, Markus (2024) AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer. NATURE COMMUNICATIONS, 15 (1): 10914. ISSN 2041-1723
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GROWTH-FACTOR RECEPTOR-3; EXPRESSION |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Urologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 28 Jan 2026 06:03 |
| Last Modified: | 28 Jan 2026 06:03 |
| URI: | https://pred.uni-regensburg.de/id/eprint/65143 |
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