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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.
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.
Insights
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.
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.
