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Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer:

Chiara Maria Lavinia Loeffler1, Nadina Ortiz Bruechle2, Max Jung2

  • 1Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.

European Urology Focus
|April 25, 2021
PubMed
Summary

An artificial intelligence system can predict FGFR3 gene mutations in bladder cancer directly from histology slides. This AI tool may help preselect patients for targeted therapies, improving treatment accessibility.

Keywords:
Artificial intelligenceBladder cancerDeep learningFGFR3 mutationsMolecular testing for fibroblast growth factor receptor therapy

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Area of Science:

  • Oncology
  • Genetics
  • Artificial Intelligence

Background:

  • Fibroblast growth factor receptor (FGFR) inhibitors are a key targeted therapy for bladder cancer.
  • Molecular testing for FGFR mutations is required but costly and not widely available.

Purpose of the Study:

  • To assess if an artificial intelligence (AI) system can predict FGFR3 gene mutations from routine bladder cancer histology slides.
  • To evaluate the AI system's performance against traditional pathological review.

Main Methods:

  • A deep learning network was trained on digitized hematoxylin and eosin-stained slides from the Cancer Genome Atlas (TCGA) cohort (n=327).
  • The algorithm was validated on an independent "Aachen" cohort (n=182) including various bladder cancer stages.
  • Performance was measured using the area under the receiver operating curve (AUROC) and compared to uropathologist scoring.

Main Results:

  • The AI system achieved an AUROC of 0.701 in the TCGA cohort and 0.725 in the Aachen cohort for detecting FGFR3 mutations.
  • The AI demonstrated generalization capabilities, achieving an AUROC of 0.625 on the Aachen cohort when trained on TCGA data.
  • The AI system outperformed a uropathologist in detecting FGFR3 mutations in a head-to-head comparison.

Conclusions:

  • A computer-based AI system can accurately detect FGFR3 gene alterations directly from bladder cancer histology slides.
  • This AI approach shows potential for preselecting patients for further molecular testing and targeted therapies.
  • Further validation in larger, multicenter cohorts is necessary to confirm and expand these findings.