AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer
Pierre‐Antoine Bannier, Charlie Saillard, P Mann, Maxime Touzot, Charles Maussion, Christian Matek, Niklas Klümper, Johannes Breyer, Ralph M. Wirtz, Danijel Sikic, Bernd J. Schmitz‐Dräger, Bernd Wullich, Arndt Hartmann, Sebastian Foersch, Markus Eckstein
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. Detecting FGFR3-mutant muscle-invasive and metastatic urothelial cancers (MIBC/mUC) for targeted therapy remains challenging, but clinically important. Here, the authors develop a deep-learning model to detect FGFR3 mutations in MIBC/mUC from routine histopathology slides, allowing for highly sensitive, rapid, and cost-effective pre-screening.