Artificial Intelligence–based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing?

European Urology Focus, vol. 8, pp. 472–479

Abstract

BACKGROUND: Fibroblast growth factor receptor (FGFR) inhibitor treatment has become the first clinically approved targeted therapy in bladder cancer. However, it requires previous molecular testing of each patient, which is costly and not ubiquitously available. OBJECTIVE: To determine whether an artificial intelligence system is able to predict mutations of the FGFR3 gene directly from routine histology slides of bladder cancer. DESIGN, SETTING, AND PARTICIPANTS: We trained a deep learning network to detect FGFR3 mutations on digitized slides of muscle-invasive bladder cancers stained with hematoxylin and eosin from the Cancer Genome Atlas (TCGA) cohort (n = 327) and validated the algorithm on the "Aachen" cohort (n = 182; n = 121 pT2-4, n = 34 stroma-invasive pT1, and n = 27 noninvasive pTa tumors). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary endpoint was the area under the receiver operating curve (AUROC) for mutation detection. Performance of the deep learning system was compared with visual scoring by an uropathologist. RESULTS AND LIMITATIONS: In the TCGA cohort, FGFR3 mutations were detected with an AUROC of 0.701 (p < 0.0001). In the Aachen cohort, FGFR3 mutants were found with an AUROC of 0.725 (p < 0.0001). When trained on TCGA, the network generalized to the Aachen cohort, and detected FGFR3 mutants with an AUROC of 0.625 (p = 0.0112). A subgroup analysis and histological evaluation found highest accuracy in papillary growth, luminal gene expression subtypes, females, and American Joint Committee on Cancer (AJCC) stage II tumors. In a head-to-head comparison, the deep learning system outperformed the uropathologist in detecting FGFR3 mutants. CONCLUSIONS: Our computer-based artificial intelligence system was able to detect genetic alterations of the FGFR3 gene of bladder cancer patients directly from histological slides. In the future, this system could be used to preselect patients for further molecular testing. However, analyses of larger, multicenter, muscle-invasive bladder cancer cohorts are now needed in order to validate and extend our findings. PATIENT SUMMARY: In this report, a computer-based artificial intelligence (AI) system was applied to histological slides to predict genetic alterations of the FGFR3 gene in bladder cancer. We found that the AI system was able to find the alteration with high accuracy. In the future, this system could be used to preselect patients for further molecular testing.

Authors 11

1. Chiara Maria Lavinia Loeffler Aachen Department of Medicine III

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

2. Nadina Ortiz Bruechle Aachen Institute of Pathology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany

3. Max Jung Aachen Institute of Pathology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany

4. Lancelot Seillier Aachen Institute of Pathology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany

5. Michael Rose Aachen Institute of Pathology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany

6. Narmin Ghaffari Laleh Aachen Department of Medicine III

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

7. Ruth Knuechel Aachen Institute of Pathology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany

8. Titus Josef Brinker

   German Cancer Research Center · National Center for Tumor Diseases

   Affiliation as printed

   Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany

9. Christian Trautwein Aachen Department of Medicine III

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

10. Nadine Therese Gaisa corresponding Aachen Institute of Pathology

    Universitätsklinikum Aachen · RWTH Aachen University

    Affiliation as printed

    Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany. Electronic address: ngaisa@ukaachen.de

    Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany

11. Jakob Nikolas Kather Aachen Department of Medicine III

    University of Leeds · Heidelberg University · University Hospital Heidelberg · National Center for Tumor Diseases · Universitätsklinikum Aachen · RWTH Aachen University

    Affiliation as printed

    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK

    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

    Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany

    Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK