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


Journal

European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661

Informations de publication

Date de publication:
03 2022
Historique:
received: 07 03 2021
revised: 30 03 2021
accepted: 08 04 2021
pubmed: 26 4 2021
medline: 9 6 2022
entrez: 25 4 2021
Statut: ppublish

Résumé

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. To determine whether an artificial intelligence system is able to predict mutations of the FGFR3 gene directly from routine histology slides of bladder cancer. 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). 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. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 33895087
pii: S2405-4569(21)00113-9
doi: 10.1016/j.euf.2021.04.007
pii:
doi:

Substances chimiques

FGFR3 protein, human EC 2.7.10.1
Receptor, Fibroblast Growth Factor, Type 3 EC 2.7.10.1

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

472-479

Informations de copyright

Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.

Auteurs

Chiara Maria Lavinia Loeffler (CML)

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

Nadina Ortiz Bruechle (N)

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

Max Jung (M)

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

Lancelot Seillier (L)

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

Michael Rose (M)

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

Narmin Ghaffari Laleh (NG)

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

Ruth Knuechel (R)

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

Titus J Brinker (TJ)

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

Christian Trautwein (C)

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

Nadine T Gaisa (NT)

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

Jakob N Kather (JN)

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.

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